- March 28, Grace Gao (UIUC)
- Apr 4, Jon Kelly (University of Toronto)
- Apr 11, Sonia Chernova (Georgia Tech)
- Apr 18, Julie Shah (MIT)
- Apr 25, Jonathan Clark (Florida State University)
- May 2, Robert Platt (Northeastern University)
- May 16, Aaron Courville (Université de Montréal)
March 28, Grace Gao, UIUC Robust Navigation: From UAVs to Robot Swarms
Robust navigation is critical and challenging for the ever-growing applications of robotics. Take Unmanned Aerial Vehicles (UAVs) as an example: the boom in applications of low-cost multi-copters requires UAVs to navigate in urban environments at low altitude. Traditionally, a UAV is equipped with a GPS receiver for outdoor flight. It may suffer from GPS signal blockage and multipath issues, making GPS-based positioning erroneous or unavailable. Moreover, GPS signals are vulnerable against attacks, such as jamming or spoofing. These attacks either disable GPS positioning, or more deliberately mislead the UAV with wrong positioning.
In this talk, we present our recent work on robust UAV navigation. We deeply fuse GPS information with Lidar, camera vision and inertial measurements on the raw signal level. In addition, we turn the unwanted multipath signals into an additional useful signal source. Instead of one GPS receiver, we use multiple receivers either on the same UAV platform or across a wide area to further improve navigation accuracy, reliability and resilience to attacks.
The second part of the talk will address our work on navigating a swarm of 100 robots, designed and built in our lab. We call them “Shinerbots,” because they are inspired by the schooling behaviors of Golden Shiner Fish. We will demonstrate the successful navigation and environment exploration of our Shinerbot swarm.
Bio:Grace Xingxin Gao is an assistant professor in the Aerospace Engineering Department at University of Illinois at Urbana-Champaign. She obtained her Ph.D. degree in Electrical Engineering from the GPS Laboratory at Stanford University. Prof. Gao has won a number of awards, including RTCA William E. Jackson Award and Institute of Navigation Early Achievement Award. She was named one of 50 GNSS Leaders to Watch by the GPS World Magazine. She has won Best Paper/Presentation of the Session Awards 11 times at ION GNSS+ conferences. She received Dean's Award for Excellence in Research from College of Engineering, University of Illinois at Urbana-Champaign. For her teaching, Prof. Gao has been on the List of Teachers Ranked as Excellent by Their Students at University of Illinois multiple times. She won the College of Engineering Everitt Award for Teaching Excellence at University of Illinois at Urbana-Champaign in 2015. She was chosen as American Institute of Aeronautics and Astronautics (AIAA) Illinois Chapter’s Teacher of the Year in 2016.
POSTPONED DUE TO SNOWMarch 14, Julie Shah, MIT Enhancing Human Capability with Intelligent Machine Teammates
Every team has top performers -- people who excel at working in a team to find the right solutions in complex, difficult situations. These top performers include nurses who run hospital floors, emergency response teams, air traffic controllers, and factory line supervisors. While they may outperform the most sophisticated optimization and scheduling algorithms, they cannot often tell us how they do it. Similarly, even when a machine can do the job better than most of us, it can’t explain how. In this talk I share recent work investigating effective ways to blend the unique decision-making strengths of humans and machines. I discuss the development of computational models that enable machines to efficiently infer the mental state of human teammates and thereby collaborate with people in richer, more flexible ways. Our studies demonstrate statistically significant improvements in people’s performance on military, healthcare and manufacturing tasks, when aided by intelligent machine teammates.
Bio:Julie Shah is an Associate Professor of Aeronautics and Astronautics at MIT and director of the Interactive Robotics Group, which aims to imagine the future of work by designing collaborative robot teammates that enhance human capability. As a current fellow of Harvard University's Radcliffe Institute for Advanced Study, she is expanding the use of human cognitive models for artificial intelligence. She has translated her work to manufacturing assembly lines, healthcare applications, transportation and defense. Before joining the faculty, she worked at Boeing Research and Technology on robotics applications for aerospace manufacturing. Prof. Shah has been recognized by the National Science Foundation with a Faculty Early Career Development (CAREER) award and by MIT Technology Review on its 35 Innovators Under 35 list. Her work on industrial human-robot collaboration was also in Technology Review’s 2013 list of 10 Breakthrough Technologies. She has received international recognition in the form of best paper awards and nominations from the ACM/IEEE International Conference on Human-Robot Interaction, the American Institute of Aeronautics and Astronautics, the Human Factors and Ergonomics Society, the International Conference on Automated Planning and Scheduling, and the International Symposium on Robotics. She earned degrees in aeronautics and astronautics and in autonomous systems from MIT.
Localization, mapping, perception, control, and trajectory planning are components of autonomous vehicle design that each have seen considerable progress in the previous three decades and especially since the first DARPA Robotics Challenge. These are areas of robotics research focused on perceiving and interacting with the external world through outward facing sensors and actuators. However, semi-autonomous driving is in many ways a human-centric activity where the at-times distracted, irrational, drowsy human may need to be included in-the-loop of safe and intelligent autonomous vehicle operation through the driver state sensing, communication, and shared control. In this talk, I will present deep neural network approaches for various subtasks of supervised vehicle autonomy with a special focus on driver state sensing and how those approaches helped us in (1) the collection, analysis, and understanding of human behavior over 100,000 miles and 1 billion video frames of on-road semi-autonomous driving in Tesla vehicles and (2) the design of real-time driver assistance systems that bring the human back into the loop of safe shared autonomy.
Bio:Lex Fridman is a postdoc at MIT, working on computer vision and deep learning approaches in the context of self-driving cars with a human-in-the-loop. His work focuses on large-scale, real-world data, with the goal of building intelligent systems that have real world impact. Lex received his BS, MS, and PhD from Drexel University where he worked on applications of machine learning, computer vision, and decision fusion techniques in a number of fields including robotics, active authentication, activity recognition, and optimal resource allocation on multi-commodity networks. Before joining MIT, Lex was at Google working on deep learning and decision fusion methods for large-scale behavior-based authentication. Lex is a recipient of a CHI-17 best paper award.
Autonomous robots are faced with a series of learning problems to optimize their behavior. In this presentation I will describe recent approaches developed in my group based on deep learning architectures for different perception problems including object recognition and segmentation and using RGB(-D) images. In addition, I will present a terrain classification approaches that utilize sound and vision. For all approaches I will describe expensive experiments quantifying in which way the corresponding approaches extend the state of the art.
Bio: Wolfram Burgard is a professor for computer science at the University of Freiburg and head of the research lab for Autonomous Intelligent Systems. His areas of interest lie in artificial intelligence and mobile robots. His research mainly focuses on the development of robust and adaptive techniques for state estimation and control. Over the past years Wolfram Burgard and his group have developed a series of innovative probabilistic techniques for robot navigation and control. They cover different aspects such as localization, map-building, SLAM, path-planning, exploration, and several other aspects. Wolfram Burgard coauthored two books and more than 300 scientific papers. In 2009, Wolfram Burgard received the Gottfried Wilhelm Leibniz Prize, the most prestigious German research award. In 2010, Wolfram Burgard received an Advanced Grant of the European Research Council. Since 2012, Wolfram Burgard is the coordinator of the Cluster of Excellence BrainLinks-BrainTools funded by the German Research Foundation. Wolfram Burgard is Fellow of the ECCAI, the AAAI, and the IEEE.
Feb 14, Sumeet Singh, Stanford University, Robust and Risk-Sensitive Planning via Contraction Theory and Convex Optimization
A key prerequisite for autonomous robots working alongside humans is the ability to cope with uncertainty at two levels: (1) low-level modeling errors or external disturbances, and (2) high-level uncertainty about the humans’ goals and actions. For the first part of this talk, I will present our framework for the online generation of robust motion plans for constrained nonlinear robotic systems such as UAVs subject to bounded disturbances while operating in cluttered environments. Specifically, by leveraging tools from contraction theory and convex optimization, we are able to provide a guaranteed margin of safety (i.e., a precise buffer zone) for any desired trajectory, thereby guaranteeing the safe, collision-free execution of the resulting motion plan. Having addressed this robust low-level control strategy, in the second part of the talk I will discuss our recent work on risk- and ambiguity- sensitive Inverse Reinforcement Learning for better capturing human decision making. In particular, by departing from the ubiquitous expected utility framework and proposing a flexible model using coherent risk metrics, we are able to capture an entire spectrum of risk preferences from risk-neutral to worst-case. This allows us to better predict the human decision making process, both qualitatively and quantitatively. We envision that leveraging such a methodology is an important step toward more reliable high- and low- level control processes for safety-critical robotics systems operating in shared environments.
Bio: Sumeet Singh is a Ph.D. candidate in Aeronautics and Astronautics at Stanford University. He received a B.Eng. in Mechanical Engineering and a Diploma of Music (Performance) from University of Melbourne in 2012, and a M.Sc. in Aeronautics and Astronautics from Stanford University in 2015. Prior to joining Stanford, Sumeet worked in the Berkeley Micromechanical Analysis and Design lab at University of California Berkeley in 2011 and the Aeromechanics Branch at NASA Ames in 2013. Sumeet’s current research interests are twofold: 1) Robust motion planning for constrained nonlinear systems, and 2) Risk-sensitive Model Predictive Control (MPC). Within the first topic, Sumeet is investigating the design of nonlinear control algorithms for online generation of robust motion plans with guaranteed margins of safety for constrained robotic systems in cluttered environments. The second topic focuses on the development and analysis of stochastic MPC algorithms for robust and risk-sensitive decision making problems.
Fall 2016 Robotics Seminars
We are witnessing a profusion of networked robotic platforms with distinct features and unique capabilities. To realize the full potential of such networked robotic systems, we need to leverage heterogeneity and complementarity through collaborative mechanisms. However, as connections are established, information is shared, and dependencies are created, these systems give rise to new vulnerabilities and threats.
To motivate the central questions of diversity, privacy, and resilience, I begin by presenting my experimental work on collaborative positioning with networked teams of robots. As the need for system-wide protection mechanisms becomes evident, I introduce a privacy model that quantifies how much is revealed to external observers about critical robotic entities and their specific interactions. My focus then shifts to the question of how to provide resilience through precautionary collaboration mechanisms, allowing robot teams to function in the presence of defective and/or malicious robots. Finally, I address the question of how to formalize diversity in the context of heterogeneous robot teams, with insights that pertain to performance.
Bio: Amanda Prorok is currently a Postdoctoral Researcher in the General Robotics, Automation, Sensing and Perception (GRASP) Laboratory at the University of Pennsylvania, where she works with Prof. Vijay Kumar on heterogeneous networked robotic systems. She completed her PhD at EPFL, Switzerland, where she addressed the topic of localization with ultra-wideband sensing for robotic networks. Her dissertation was awarded the Asea Brown Boveri (ABB) award for the best thesis at EPFL in the fields of Computer Sciences, Automatics and Telecommunications. She was selected as an MIT Rising Star in 2015, and won a Best Paper Award at BICT 2015.
As robots are deployed into less and less structured environments, it becomes increasingly important to enhance their ability to complete high-level tasks with little or no human intervention. Whether the task is to search, inspect, or navigate to target destinations, it generally involves decomposing it into discrete, logical actions, where each discrete action often requires complex collision-free and dynamically-feasible motions in order to be implemented. This talk will discuss our research efforts on a computationally-efficient framework and a formal treatment of the combined task and motion- planning problem as search over a hybrid space consisting of discrete and continuous components. The framework makes it possible to specify high-level tasks via Finite State Machines, Linear Temporal Logic, and Planning-Domain Definition Languages and automatically computes collision-free and dynamically-feasible motions that enable the robot to accomplish the assigned task. Applications in autonomous underwater vehicles will be highlighted.
Bio: Erion Plaku is an Associate Professor in the Department of Electrical Engineering and Computer Science at Catholic University of America. He received his Ph.D. degree in Computer Science from Rice University in 2008. He was a Postdoctoral Fellow in the Laboratory for Computational Sensing and Robotics at Johns Hopkins University during 2008—2010. Plaku's research is in Robotics and Artificial Intelligence, focusing on enhancing automation in human-machine cooperative tasks in complex domains, such as mobile robotics, autonomous underwater vehicles, and hybrid systems. His research is supported by NSF Intelligent Information Systems, NSF Software Infrastructure, and the U.S. Naval Research Laboratory. More information, including publications, research projects, open-source software he has developed for robot motion planning, and educational materials can be found at http://www.robotmotionplanning.org
Humanoid robots with torque control capabilities are becoming increasingly available in our research community. These robots allow for an explicit control of contact interactions which have the potential to allow robots locomote through difficult terrains. In order to accomplish tasks under balance and contact constraints, whole-body planning and control strategies are required to generate motion and force commands for all limbs efficiently.
Model-based control in combination with numerical optimization is becoming a reliable tool for efficient control of complex tasks on floating-base robots. In the first part of my talk I will discuss hierarchical inverse dynamics, a control framework that allows for the composition of complex behaviors from a hierarchy of simpler tasks and constraints. We use cascades of quadratic programs to resolve task hierarchies into joint torques in a 1kHz feedback-loop on our torque controlled humanoid. In our experiments we control the momentum of the robot embedded into a hierarchy of tasks and constraints leading to robust push recovery on our robot.
In the second part of my talk I will discuss our kino-dynamic motion generation approach for the full body. We solve an optimization program to obtain whole-body joint and contact force trajectories over a horizon that consider the full robot dynamics and contact constraints. We decompose this non-convex optimization problem into two better structured mathematical programs that are solved iteratively with better informed solvers. Our analysis reveals structure in the centroidal momentum dynamics of floating-base robots that leads to new efficient solvers on the full humanoid model. We can improve the speed of naive off-the shelf solvers by an order of magnitude and phrase direct shooting methods on centroidal dynamics with linear time complexity.
There recently is, again, substantial optimism about AI. While I welcome and share the general enthusiasm, I believe that the great advances in machine learning and data-driven methods alone cannot solve fundamental problems in real-world robotic AI. A core challenge remains to capture and formalize essential structure in real-world decision making and manipulation problems, and thereby provide the foundation for sample-efficient learning. In this talk I will discuss three concrete pieces of work in this context: autonomously exploring the environment to learn what is manipulable and how; learning manipulation skills from few demonstrations; and learning sequential manipulation and cooperative assembly from demonstration. All three applications raise fundamental challenges especially w.r.t. the problem formulation and thereby guide us in what we think are interesting research questions to progress the field towards robotic AI.
Bio: Marc Toussaint is full professor for Machine Learning and Robotics at the University of Stuttgart since 2012. Before that he was assistant professor at the Free University Berlin, leading an Emmy Noether research group at TU Berlin, and spend two years as a post-doc at the University of Edinburgh. His research focuses on the combination of decision theory and machine learning, motivated by research questions in robotics. Reoccurring themes in his research are appropriate representations (symbols, temporal abstractions, relational representations) to enable efficient learning and manipulation in real world environments, and how to achieve jointly geometric, logic and probabilistic learning and reasoning. He currently is coordinator of the German research priority programme on Autonomous Learning, member of the editorial board of the Journal of AI Research (JAIR), reviewer for the German Research Foundation, and programme committee member of several top conferences in the field (UAI, R:SS, ICRA, IROS, AIStats, ICML). His work was awarded best paper at R:SS'12, ICMLA'07 and runner up at UAI'08.
Nov 15 - Inna Sharf, McGill University, Towards Greater Autonomy and Safety of UAVs: Recovering from Collisions
Making small unmanned aerial vehicles more autonomous is a continuing endeavour in the UAV research community; it is also the focus of Sharf’s research. In this context, her group has been working on problems of state estimation, localization and mapping, system integration and controller design for multicopters and indoor blimps. Following a brief overview of past research projects, this presentation will focus on current work on the development of collision recovery controllers for quadcopters. The collision dynamics model and post collision response characterization of the quadrotor are presented, followed by their experimental validation. A collision recover pipeline is proposed to allow propeller protected quadrotors to recover from a collision. This pipeline includes collision detection, impact characterization and aggressive attitude control. The strategy is validated via a comprehensive Monte Carlo simulation of collisions against a wall, showing the feasibility of recovery from challenging collision scenarios. The pipeline is implemented on a custom quadrotor platform, demonstrating feasibility of real-time performance and successful recovery from a range of pre-collision conditions. The ultimate goal is to implement a general collision recovery solution to further advance the autonomy and safety of quadrotor vehicles.
Bio: Dr. Inna Sharf is a professor in the Department of Mechanical Engineering at McGill University, Montreal, Canada. She received her B.A.Sc. in Engineering Science from the University of Toronto (1D at the Institute for Aerospace Studies, University of Toronto (1991). Prior to relocating to McGill in 2001, she was on faculty with the Department of Mechanical Engineering at the University of Victoria. Sharf’s research activities are in the areas of dynamics and control with applications to space robotic systems, unmanned aerial vehicles and legged robots. Sharf has published over 150 conference and journal papers on her academic research. She is an associate fellow of AIAA and a member of IEEE.
In this talk, we present control and planning algorithms for autonomous vehicles that deliver high performance. In the first part of the talk, we focus on control problems for vehicle-level autonomy, i.e., for controlling a single vehicle with complex dynamics to execute complex tasks. Specifically, we introduce novel algorithms that construct arbitrarily good solutions for stochastic optimal control problems. We show that their running time scales linearly with dimension and polynomially with the rank of the optimal cost-to-go function, breaking the curse of dimensionality for low-rank problems. Our results are enabled by a novel continuous analogue of the well-known tensor-train decomposition. We demonstrate the new algorithms on a simulated perching problem, where the computational savings reach ten orders of magnitude when compared to naive approaches, such as value iteration on a grid. In the second part of the talk, we focus on system-level autonomy, i.e., problems that concern systems that consist of several autonomous vehicles. Specifically, we present results on optimal coordination of vehicles passing through an intersection. We reduce the problem to a polling system, under mild technical conditions. We show that the resulting system provides orders of magnitude improvement in delay, when compared to conventional traffic light systems.
Bio: Sertac Karaman is an Associate Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology. He has obtained B.S. degrees in mechanical engineering and in computer engineering from the Istanbul Technical University, Turkey, in 2007; an S.M. degree in mechanical engineering from MIT in 2009; and a Ph.D. degree in electrical engineering and computer science also from MIT in 2012. His research interests lie in the broad areas of robotics and control theory. In particular, he studies the applications of probability theory, stochastic processes, stochastic geometry, formal methods, and optimization for the design and analysis of high-performance cyber-physical systems. The application areas of his research include driverless cars, unmanned aerial vehicles, distributed aerial surveillance systems, air traffic control, certification and verification of control systems software, and many others. He is the recipient of an Army Research Office Young Investigator Award in 2015, National Science Foundation Faculty Career Development (CAREER) Award in 2014, AIAA Wright Brothers Graduate Award in 2012, and an NVIDIA Fellowship in 2011.
Despite decades of research, real-time, general-purpose robot motion planning remains beyond our reach. A solution to this problem is critical to dealing with natural environments that are not carefully controlled or designed, and our inability to solve it is a major obstacle preventing the widespread deployment of robots in the workplace and the home. I will describe recent research that aims to solve the real-time motion planning problem through the use of a specialized hardware: a custom processor designed solely and specifically to perform motion planning, capable of finding plans for interesting robots in less than one millisecond, while consuming less than 15 watts. I will describe the design of this processor, the research questions and trade-offs that that design induces, and the new potential capabilities created by the ability to find thousands of plans per second. (Collaborative research with Sean Murray, Will Floyd-Jones, Ying Qi, and Dan Sorin, all of Duke University.)
Bio: George Konidaris is an Assistant Professor of Computer Science at Brown. Before joining Brown, he was on the faculty at Duke, and a postdoctoral researcher at MIT. George holds a PhD in Computer Science from the University of Massachusetts Amherst, an MSc in Artificial Intelligence from the University of Edinburgh, and a BScHons in Computer Science from the University of the Witwatersrand. He is the recent recipient of Young Faculty Awards from DARPA and the AFOSR.
Oct 25 - Alberto Rodriguez, MIT Feedback Control of the Pusher-Slider: A Story of Hybrid and Underactuated Contact Dynamics
In this talk I'll discuss ideas and ongoing work on real-time control strategies for dynamical systems that involve frictional contact interactions. Hybridness and underactuation are key characteristics of these systems that complicate the design of feedback controllers. I'll discuss these challenges and possible control solutions in the context of the pusher-slider system, where the purpose is to control the motion of an object sliding on a flat surface using a point pusher. The pusher-slider is a classical simple dynamical system with many of the challenges present in robotic manipulation tasks: noisy planar sliding friction, instability, hybridness, underactuation, ... I like to call it the simplest but still interesting problem in manipulation, a sort of "inverted pendulum" for robotic manipulation. I'll start the talk by briefing on recent work in my group's participation in the Amazon Picking Challenge, and motivate the need for closed-loop control in grasping and manipulation.
Bio: Alberto Rodriguez is the Walter Henry Gale (1929) Career Development Professor at the Mechanical Engineering Department at MIT. Alberto graduated in Mathematics ('05) and Telecommunication Engineering ('06) from the Universitat Politecnica de Catalunya (UPC) in Barcelona, and earned his PhD in Robotics (’13) from the Robotics Institute at Carnegie Mellon University. After spending a year in the Locomotion group at MIT, he joined the faculty at MIT in 2014, where he started the Manipulation and Mechanisms Lab (MCube). Alberto is the recipient of the Best Student Paper Awards at conferences RSS 2011 and ICRA 2013 and Best Paper finalist at IROS 2016. His main research interests are in robotic manipulation, mechanical design, and automation. His long-term research goal is to provide robots with enough sensing, reasoning and acting capabilities to reliably manipulate their environment.
Oct 18 - Tom Howard, University of Rochester Learning Models for Robot Decision Making
The efficiency and optimality of robot decision making is often dictated by the fidelity and complexity of models for how a robot can interact with its environment. It is common for researchers to engineer these models a priori to achieve particular levels of performance for specific tasks in a restricted set of environments and initial conditions. As we progress towards more intelligent systems that perform a wider range of objectives in a greater variety of domains, the models for how robots make decisions must adapt to achieve, if not exceed, engineered levels of performance. In this talk I will discuss progress towards model adaptation for robot intelligence, including recent efforts in natural language understanding for human-robot interaction.
Bio: Thomas Howard is an assistant professor in the Department of Electrical and Computer Engineering and the Department of Computer Science at the University of Rochester. He is also a member of the Georgen Institute for Data Science and holds a secondary appointment in the Department of Biomedical Engineering. Previously he held appointments as a research scientist and a postdoctoral associate at MIT's Computer Science and Artificial Intelligence Laboratory in the Robust Robotics Group, a research technologist at the Jet Propulsion Laboratory in the Robotic Software Systems Group, and a lecturer in mechanical engineering at Caltech.
Howard earned a PhD in robotics from the Robotics Institute at Carnegie Mellon University in 2009 in addition to BS degrees in electrical and computer engineering and mechanical engineering from the University of Rochester in 2004. His research interests span artificial intelligence, robotics, and human-robot interaction with particular research focus on improving the optimality, efficiency, and fidelity of models for decision making in complex and unstructured environments with applications to robot motion planning and natural language understanding. He has applied his research on numerous robots including planetary rovers, autonomous automobiles, mobile manipulators, robotic torsos, and unmanned aerial vehicles. Howard was a member of the flight software team for the Mars Science Laboratory, the motion planning lead for the JPL/Caltech DARPA Autonomous Robotic Manipulation team, and a member of Tartan Racing, winner of the DARPA Urban Challenge.
Oct 4 - Brenna Argall, Northwestern University Human Autonomy through Robotics Autonomy
It is a paradox that often the more severe a person's motor impairment, the more assistance they require and yet the less able they are to operate the very assistive machines created provide this assistance. A primary aim of my lab is to address this confound by incorporating robotics autonomy and intelligence into assistive machines---to offload some of the control burden from the user. Robots already synthetically sense, act in and reason about the world, and these technologies can be leveraged to help bridge the gap left by sensory, motor or cognitive impairments in the users of assistive machines. However, here the human-robot team is a very particular one: the robot is physically supporting or attached to the human, replacing or enhancing lost or diminished function. In this case getting the allocation of control between the human and robot right is absolutely essential, and will be critical for the adoption of physically assistive robots within larger society. This talk will overview some of the ongoing projects and studies in my lab, whose research lies at the intersection of artificial intelligence, rehabilitation robotics and machine learning. We are working with a range of hardware platforms, including smart wheelchairs and assistive robotic arms. A distinguishing theme present within many of our projects is that the machine automation is customizable---to a user's unique and changing physical abilities, personal preferences or even financial means.
Bio: Brenna Argall is the June and Donald Brewer Junior Professor of Electrical Engineering & Computer Science at Northwestern University, and also an assistant professor in the Department of Mechanical Engineering and the Department of Physical Medicine & Rehabilitation. Her research lies at the intersection of robotics, machine learning and human rehabilitation. She is director of the assistive & rehabilitation robotics laboratory (argallab) at the Rehabilitation Institute of Chicago (RIC), the premier rehabilitation hospital in the United States, and her lab's mission is to advance human ability through robotics autonomy. Argall is a 2016 recipient of the NSF CAREER award. Her Ph.D. in Robotics (2009) was received from the Robotics Institute at Carnegie Mellon University, as well as her M.S. in Robotics (2006) and B.S. in Mathematics (2002). Prior to joining Northwestern, she was a postdoctoral fellow (2009-2011) at the École Polytechnique Fédérale de Lausanne (EPFL), and prior to graduate school she held a Computational Biology position at the National Institutes of Health (NIH).
Sep 20 - Dorsa Sadigh, UC Berkeley Towards a Theory of Human-Cyber-Physical Systems
The goal of my research is to enable safe and reliable integration of Human-Cyber-Physical Systems (h-CPS) in our society by providing a unified framework for modeling and design of these systems. Today’s society is rapidly advancing towards CPS that interact and collaborate with humans, e.g., semiautonomous vehicles interacting with drivers and pedestrians, medical robots used in collaboration with doctors, or service robots interacting with their users in smart homes. The safety-critical nature of these systems requires us to provide provably correct guarantees about their performance. I aim to develop a formalism for design of algorithms and mathematical models that enable correct-by-construction control and verification of h-CPS.
In this talk, I will focus on two natural instances of this agenda. I will first talk about interaction-aware control, where we use algorithmic HRI to be mindful of the effects of autonomous systems on humans, and further leverage these effects for better safety and efficiency. I will then talk about providing correctness guarantees while taking into account the uncertainty arising from the environment. Through this effort, I will introduce Probabilistic Signal Temporal Logic (PrSTL), an expressive specification language that allows representing Bayesian graphical models as part of its predicates. Then, I will provide a solution for synthesizing controllers that satisfy PrSTL specifications, and further discuss a diagnosis and repair algorithm for systematic transfer of control to the human in unrealizable settings. While the algorithms and techniques introduced can be applied to many h-CPS applications, in this talk, I will focus on the implications of my work for semiautonomous driving.
Bio: Dorsa Sadigh is a Ph.D. candidate in the Electrical Engineering and Computer Sciences department at UC Berkeley. Her research interests lie in the intersection of control theory, formal methods and human-robot interactions. Specifically, she works on developing a unified framework for safe and reliable design of human-cyber-physical systems. Dorsa received her B.S. from Berkeley EECS in 2012. She was awarded the NDSEG and NSF graduate research fellowships in 2013. She was the recipient of the 2016 Leon O. Chua department award and the 2011 Arthur M. Hopkin department award for achievement in the field of nonlinear science, and she received the Google Anita Borg Scholarship in 2016.
Everything about a robot is unreliable: sensors lie, state estimators compute poor means and variances, and actuators slip and slide. It is too much to ask these systems to be 100% reliable, but then how do we build incredibly reliable systems that can operate for 100 million miles between serious mishap, or those that can inhabit a house alongside people without occasionally running over the cats?
In this talk, I describe two different approaches that allow robots to tolerate failures, moving us away from the need for 100% reliability. The first is an probabilistic inference system (Max-Mixtures) that allows us to model non-Gaussian sensor failures. Max-Mixtures can be used to unify outlier rejection and state estimation or to do inference when sensor data is multi-modal, but they are nearly as fast as ordinary least squares methods. The second approach is a planning approach (Multi-Policy Decision Making, MPDM) that allows a robot to introspectively choose between multiple ways of performing a task, selecting the more reliable approach. For example, a robot might choose to visually servo towards a target instead of trajectory planning through a 3D model acquired from LIDAR. In short, the robot does the easy dumb thing when it can, and resorts to the complex thing when it must.
In this talk, I will describe our recent progress in developing fault-tolerant distributed control policies for multi-robot systems. We consider two problems: rendezvous and coverage. For the former, the goal is to bring all robots to a common location, while for the latter the goal is to deploy robots to achieve optimal coverage of an environment. We consider the case in which each robot is an autonomous decision maker that is anonymous, memoryless, and dimensionless, i.e., robots are indistinguishable to one another, make decisions based upon only current information, and do not consider collisions. Each robot has a limited sensing range, and is able to directly estimate the state of only those robots within that sensing range, which induces a network topology for the multi-robot system. We assume that it is not possible for the fault-free robots to identify the faulty robots (e.g., due to the anonymous property of the robots). For each problem, we provide an efficient computational framework and analysis of algorithms, all of which converge in the face of faulty robots under a few assumptions on the network topology and sensing abilities.
Bio: Seth Hutchinson received his Ph.D. from Purdue University in 1988. In 1990 he joined the faculty at the University of Illinois in Urbana-Champaign, where he is currently a Professor in the Department of Electrical and Computer Engineering, the Coordinated Science Laboratory, and the Beckman Institute for Advanced Science and Technology. He served as Associate Department Head of ECE from 2001 to 2007. He currently serves on the editorial boards of the International Journal of Robotics Research and the Journal of Intelligent Service Robotics, and chairs the steering committee of the IEEE Robotics and Automation Letters. He was Founding Editor-in-Chief of the IEEE Robotics and Automation Society's Conference Editorial Board (2006-2008), and Editor-in-Chief of the IEEE Transaction on Robotics (2008-2013). He has published more than 200 papers on the topics of robotics and computer vision, and is coauthor of the books "Principles of Robot Motion: Theory, Algorithms, and Implementations," published by MIT Press, and "Robot Modeling and Control," published by Wiley. Hutchinson is a Fellow of the IEEE.
Humans effortlessly push, pull, and slide objects, fearlessly reconfiguring clutter, and using physics and the world as a helping hand. But most robots treat the world like a game of pick-up-sticks: avoiding clutter and attempting to rigidly grasp anything they want to move. I'll talk about some of our ongoing efforts at harnessing physics for nonprehensile manipulation, and the challenges of deploying our algorithms on real physical systems. I'll specifically focus on whole-arm manipulation, state estimation for contact manipulation, and on closing the feedback loop on nonprehensile manipulation.
Recent years have witnessed tremendous progress in the development of autonomous machines. Autonomous cars have driven over millions of miles, and robots now regularly perform tasks too dangerous or monotonous for human beings. Yet despite these advancements, robots continue to remain highly dependent on human operators and carefully designed environments. In one prominent example, the DARPA Robotics Challenge asked dozens of participating robots to complete tasks in a mock disaster response scenario. But all teams, lacking confidence in their robot?s ability to reliably perceive its surroundings, opted to outsource most perception to humans. Team KAIST, the eventual winner, "found that the most (actually all) famous algorithms are not very effective in real situations."
In this talk, I will address the endeavor of bridging the gap between computer vision and robot perception, summarizing my experiences in three design principles. First, I will argue that it is crucial for the algorithms to fully operate end-to-end in three-dimensions, establishing the grounds for the area of "3D Deep Learning". I will demonstrate this idea on object detection, view planning, and mapping in a personal robotics scenario. Second, I will highlight the importance of direct perception in estimating affordances for a robot's actions, demonstrating the idea in an autonomous driving application. Third, I will propose the design of robot systems with failure modes of perception in mind, allowing for pitfall avoidance and an extremely high level of robustness. Finally, going beyond perception, I will briefly mention some ongoing works in Big Data Robotics, Robot Learning, and Human Robot Collaboration.
Bio: Jianxiong Xiao is an Assistant Professor in the Department of Computer Science at Princeton University. He received his Ph.D. from the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology (MIT) in 2013. Before that, he received a BEng. and MPhil. in Computer Science from the Hong Kong University of Science and Technology in 2009. His research focuses on bridging the gap between computer vision and robotics by building extremely robust and dependable computer vision systems for robot perception. In particular, he is interested in 3D Deep Learning, RGB-D Recognition and Mapping, Deep Learning for Robotics, Autonomous Driving, Big Data Robotics, and Robot Learning. His work has received the Best Student Paper Award at the European Conferenceon Computer Vision (ECCV) in 2012 and the Google Research Best Papers Award for 2012, and has appeared in the popular press. Jianxiong was awarded the Google U.S./Canada Fellowship in Computer Vision in 2012, the MIT CSW Best Research Award in 2011, and two Google Faculty Awards in 2014 and in 2015 respectively. More information can be found at http://vision.princeton.edu.
Roboticists have addressed increasingly complicated motion planning challenges over the last decades. A popular paradigm related to this achievement corresponds to sampling and graph-based solutions, for which the conditions to achieve asymptotic optimality have been recently identified. In this domain, we have contributed a study on the practical properties of these planners after finite computation time and how sparse representations can guarantee to efficiently return near-optimal solutions. We have also proposed the first method that achieves asymptotic optimality for kinodynamic planning without access to a steering function, which can impact high-dimensional belief space planning. After reviewing these contributions, this talk will discuss recent work on manipulation task planning challenges. In particular, we will present a methodology for efficiently rearranging multiple similar objects using a robotic arm. The talk will conclude on how such algorithmic progress together with technological developments bring the hope of reliably deploying robots in important applications, ranging from space exploration to warehouse automation and logistics.
Bio: Kostas Bekris is an Assistant Professor of Computer Science at Rutgers University. He completed his PhD degree in Computer Science at Rice University, Houston, TX, under the supervision of Prof. Lydia Kavraki. He was Assistant Professor at the University of Nevada, Reno between 2008 and 2012. He works in robotics and his interests include motion planning, especially for systems with dynamics, manipulation, online replanning, motion coordination, and applications in cyber-physical systems and simulations. His research group has been supported by NSF, NASA (Early CAREER Faculty award), DHS, DoD and the NY/NJ Port Authority.
A critical aspect of human intelligence is the ability to learn to make good decisions. Achieving similar behavior in artificial agents is a key focus in AI, and could have enormous benefits, particularly in applications like education and healthcare where autonomous agents could help people expand their capacity and reach their potential. But tackling such domains requires approaches that can handle the noisy, stochastic, costly decisions that characterize interacting with people. In this talk I will describe some of our recent work in pursuing this agenda. One key focus has been on offline policy evaluation, how to use old data to estimate the performance of different strategies, and I will discuss a new estimator that can yield orders of magnitude smaller mean squared estimates. I will also describe how problems like transfer learning and partially observable reinforcement learning can be framed as instances of latent variable modeling for control, and enable new sample complexity results for these settings. Our advances in these topics have enabled us to obtain more engaging educational games and better news recommendations.
Bio: Emma Brunskill is an assistant professor of computer science and an affiliate professor of machine learning at Carnegie Mellon University. She is a Rhodes Scholar, a Microsoft Faculty Fellow, a NSF CAREER awardee and a ONR Young Investigator Program recipient. Her work has been recognized with best paper nominations at the Educational Data Mining conference (2012,2013) and the Computer Human Interaction conference (2014), and a best paper award at the Reinforcement Learning and Decision Making Conference (2015).
March 8 - Cynthia Sung, MIT Computational Tools for Robot Design: A Composition Approach (video)
As robots become more prevalent in society, they must develop an ability to deal with more diverse situations. This ability entails customizability of not only software intelligence, but also of hardware. However, designing a functional robot remains challenging and often involves many iterations of design and testing even for skilled designers. My goal is to create computational tools for making functional machines, allowing future designers to quickly improvise new hardware.
In this talk, I will discuss one possible approach to automated design using composition. I will describe our origami-inspired print-and-fold process that allows entire robots to be fabricated within a few hours, and I will demonstrate how foldable modules can be composed together to create foldable mechanisms and robots. The modules are represented parametrically, enabling a small set of modules to describe a wide range of geometries and also allowing geometries to be optimized in a straightforward manner. I will also introduce a tool that we have developed that combines this composition approach with simulations to help human designers of all skill levels to design and fabricate custom functional robots.
Bio: Cynthia Sung is a Ph.D. candidate working with Prof. Daniela Rus in the Computer Science and Artificial Intelligence Laboratory at the
Massachusetts Institute of Technology (MIT). She received a B.S. in Mechanical Engineering from Rice University in 2011 and an M.S. in
Electrical Engineering and Computer Science from MIT in 2013. Cynthia is a recipient of the NDSEG and NSF graduate fellowships. Her research
interests include computational design, folding theory, and rapid fabrication, and her current work focuses on algorithms for synthesis
and analysis of engineering designs.
Motion planning – the problem of computing physical actions to complete a specified task – is a fundamental problem in robotics, and has inspired some of the most rigorous and beautiful theoretical results in robotics research. But as robots proliferate in real-world applications like household service, driverless cars, warehouse automation, minimally-invasive surgery, search-and-rescue, and unmanned aerial vehicles, we are beginning to see the classical theory falter in light of the new reality of modern robotics practice. Today’s robots must handle large amounts of noisy sensor data, uncertainty, underspecified models, nonlinear and hysteretic dynamic effects, exotic objective functions and constraints, and real-time demands. This talk will present recent efforts to bring motion planners to bear on real robots, along four general directions 1) improving planning algorithm performance, 2) broadening the scope of problems that can be addressed by planners, 3) incorporating richer, higher fidelity models into planning, and 4) improved workflows for integrating planners into robot systems. This research is applied to a variety of systems, including ladder climbing in the DARPA Robotics Challenge, the Duke rock-climbing robot project, semiautonomous mobile manipulators, and object manipulation in the Amazon Picking Challenge.
Bio: Kris Hauser is an Associate Professor at the Pratt School of Engineering at Duke University with a joint appointment in the Electrical and Computer Engineering Department and the Mechanical Engineering and Materials Science Department. He received his PhD in Computer Science from Stanford University in 2008, bachelor's degrees in Computer Science and Mathematics from UC Berkeley in 2003, and worked as a postdoctoral fellow at UC Berkeley. He then joined the faculty at Indiana University from 2009-2014, where he started the Intelligent Motion Lab, and began his current position at Duke in 2014. He is a recipient of a Stanford Graduate Fellowship, Siebel Scholar Fellowship, Best Paper Award at IEEE Humanoids 2015, and an NSF CAREER award.
Research interests include robot motion planning and control, semiautonomous robots, and integrating perception and planning, as well as applications to intelligent vehicles, robotic manipulation, robot-assisted medicine, and legged locomotion.
Abstract: Recent technological advances have given way to a new generation of versatile legged robots. These machines are envisioned to replace first responders in disaster scenarios and enable unmanned exploration of distant planets. To achieve these aims, however, our robots must be able to manage physical interaction through contact to move through unstructured terrain. This talk reports on the development of control systems for legged robots to achieve unprecedented levels of dynamic mobility by addressing many critical problems for contact interaction with the environment. Drawing on key insights from biomechanics, the talk will open with a description of optimization-based balance control algorithms for high-speed locomotion in humanoid robots. It will then present design features of the MIT Cheetah 2 quadruped robot that enable dynamic locomotion in experimental hardware. A model predictive control framework for this robot will be described which enables the Cheetah to autonomously jump over obstacles with a maximum height of 40 cm (80% of leg length) while running at 2.5 m/s. Across these results, dynamic physical interaction with the environment is exploited, rather than avoided, to achieve new levels of performance.
Drones hold enormous potential for consumer video, inspection, mapping, monitoring, and perhaps even delivery. They’re also natural candidates for autonomy and likely to be among the first widely-deployed systems that incorporate meaningful intelligence based on computer vision and robotics research. In this talk I’ll discuss the trajectory of hobbies, research, and work that led me to start Skydio. I’ll cover some of the algorithms developed during my research at MIT which culminated in a fixed-wing vehicle that could navigate obstacles at high speeds. I’ll also present some of the work that we’ve done at Skydio in motion planning and perception, along with the challenges involved in building a robust robotics software system that needs to work at scale.
Bio: Adam Bry is co-founder and CEO of Skydio, a venture backed drone startup based in the bay area. Prior to Skydio he helped start Project Wing at Google[x] where he worked on the flight algorithms and software. He holds a SM in Aero/Astro from MIT and a BS in Mechanical Engineering from Olin College. Adam grew up flying radio controlled airplanes and is a former national champion in precision aerobatics.
February 9 - Rob Wood, Harvard Manufacturing, actuation, sensing, and control for robotic insects
As the characteristic size of a flying robot decreases, the challenges for successful flight revert to basic questions of fabrication, actuation, fluid mechanics, stabilization, and power -- whereas such questions have in general been answered for larger aircraft. When developing a robot on the scale of a housefly, all hardware must be developed from scratch as there is nothing "off-the-shelf" which can be used for mechanisms, sensors, or computation that would satisfy the extreme mass and power limitations. With these challenges in mind, this talk will present progress in the essential technologies for insect-scale robots and the latest flight experiments with robotic insects.
December 15 - Metin Sitti, Max Planck Institute Mobile Microrobotics (no video)
Untethered mobile microrobots have the unique capability of accessing to small spaces and scales directly. Due to their small size and micron-scale physics and dynamics, they could be agile and portable, and could be inexpensive and in large numbers if they are mass-produced. Mobile microrobots would have high impact applications in health-care, bioengineering, mobile sensor networks, desktop micromanufacturing, and inspection. In this presentation, mobile microrobots from few micrometers up to hundreds of micrometer overall sizes and various locomotion capabilities are presented. Going down to micron scale, one of the grand challenges for mobile microrobots is miniaturization limitation on on-board actuation, powering, sensing, processing, and communication components. Two alternative approaches are explored in this talk to solve the actuation and powering challenges. First, biological cells, e.g. bacteria, attached to the surface of a synthetic microrobot are used as on-board microactuators and microsensors using the chemical energy inside or outside the cell in physiological fluids. Bacteria-propelled randomly microswimmers are steered using chemical and pH gradients in the environment and remote magnetic fields towards future targeted drug delivery and environmental remediation applications. As the second approach, external actuation of untethered magnetic microrobots using remote magnetic fields in enclosed spaces is demonstrated. New magnetic microrobot locomotion principles based on rotational stick-slip and rolling dynamics are proposed. Novel magnetic composite materials are used to address and control teams of microrobots and to create novel soft actuators and programmable soft matter. Untethered microrobot teams are demonstrated to manipulate live cells and microgels with embedded cells for bioengineering applications, and to self-assemble into different patterns with remote magnetic control.
Bio: Metin Sitti received the BSc and MSc degrees in electrical and electronics engineering from Bogazici University, Istanbul, Turkey, in 1992 and 1994, respectively, and the PhD degree in electrical engineering from the University of Tokyo, Tokyo, Japan, in 1999. He was a research scientist at UC Berkeley during 1999-2002. He is currently a director in Max-Planck Institute for Intelligent Systems and a professor in Department of Mechanical Engineering and Robotics Institute at Carnegie Mellon University. His research interests include small-scale physical intelligence, mobile microrobots, bio-inspired millirobots, smart and soft micro/nanomaterials, and programmable self-assembly. He is an IEEE Fellow. He received the SPIE Nanoengineering Pioneer Award in 2011 and NSF CAREER Award in 2005. He received the IEEE/ASME Best Mechatronics Paper Award in 2014, the Best Poster Award in the Adhesion Conference in 2014, the Best Paper Award in the IEEE/RSJ International Conference on Intelligent Robots and Systems in 2009 and 1998, the first prize in the World RoboCup Micro-Robotics Competition in 2012 and 2013, the Best Biomimetics Paper Award in the IEEE Robotics and Biomimetics Conference in 2004, and the Best Video Award in the IEEE Robotics and Automation Conference in 2002. He is the editor-in-chief of Journal of Micro-Bio Robotics.
An enduring myth in the world of legged locomotion is that a robot should model itself upon human. The human presents a standard for performance, and a recipe for control strategy, and a blueprint for design. Not only is that myth false, but it has also (fortunately) been ignored. To date, robot locomotion has benefitted from humans and animals, and the understanding of them, only in how many legs to have. The reason is that hardware technology is presently far from making truly human-like locomotion possible, or even a good idea. This raises the question of what the next generation of legged robots should try to be. The correct answer is anything but humans, but even to achieve that means there is reason to understand humans. I will demonstrate a few unique ways that humans walk dynamically, and how they are optimal for humans and therefore suboptimal for robots. From a biomechanical perspective, I will muse on some interesting challenges for future robots that will act more dynamically, and one day perhaps even approach the standard set by humans.
Bio: Art Kuo is Professor of Mechanical Engineering and Biomedical Engineering at the University of Michigan. He directs the Human Biomechanics and Control Laboratory, which studies the basic principles of locomotion and other movements, and applies those principles to the development of robotic, assistive, and therapeutic devices to aid humans. Current interests include walking and running on uneven terrain, development of wearable sensors to track foot motion in the wild, and devices to improve the economy of locomotion in the impaired.
December 1 - Louis Whitcomb, Johns Hopkins Nereid Under-Ice: A Remotely Operated Underwater Robotic Vehicle for Oceanographic Access Under Ice
This talk reports recent advances in underwater robotic vehicle research to enable novel oceanographic operations in extreme ocean environments, with focus on two recent novel vehicles developed by a team comprised of the speaker and his collaborators at the Woods Hole Oceanographic Institution. First, the development and operation of the Nereus underwater robotic vehicle will be briefly described, including successful scientific observation and sampling dive operations at hadal depths of 10,903 m. on a NSF sponsored expedition to the Challenger Deep of the Mariana Trench – the deepest place on Earth. Second, development and first sea trials of the new Nereid Under-Ice (UI) underwater vehicle will be described. NUI is a novel remotely-controlled underwater robotic vehicle capable of being teleoperated under ice under remote real-time human supervision. We report the results of NUI’s first under-ice deployments during a July 2014 expedition aboard R/V Polarstern at 83° N 6 W° in the Arctic Ocean – approximately 200 km NE of Greenland.
Bio: Louis L. Whitcomb is Professor and Chairman at the Department of Mechanical Engineering, with secondary appointment in Computer Science, at the Johns Hopkins University’s Whiting School of Engineering. He completed a B.S. in Mechanical Engineering in 1984 and a Ph.D. in Electrical Engineering in 1992 at Yale University. From 1984 to 1986 he was a Research and Development engineer with the GMFanuc Robotics Corporation in Detroit, Michigan. He joined the Department of Mechanical Engineering at the Johns Hopkins University in 1995, after post doctoral fellowships at the University of Tokyo and the Woods Hole Oceanographic Institution. His research focuses on the navigation, dynamics, and control of robot systems – including industrial, medical, and underwater robots. Whitcomb is a principal investigator of the Nereus and Nereid Under-Ice Projects. He is former (founding) Director of the JHU Laboratory for Computational Sensing and Robotics. He received teaching awards at Johns Hopkins in 2001, 2002, 2004, and 2011, was awarded a National Science Foundation Career Award, and an Office of Naval Research Young Investigator Award. He is a Fellow of the IEEE. He is also Adjunct Scientist, Department of Applied Ocean Physics and Engineering, Woods Hole Oceanographic Institution.
In this talk, we investigate the area coverage problem with mobile robots whose localization uncertainty is time-varying and significant.
The vast majority of literature on robotics area coverage assumes that the robot's location estimate error is either zero or at least bounded.
We remove this assumption and develop a probabilistic representation of coverage. Once we have formally connected robot sensor uncertainty with the area coverage, we motivate an adaptive sliding window filter pose estimator that is able to provide an arbitrarily close approximate to the full maximum a posteriori estimation with a computation cost that does not grow with time. An adaptive planning strategy is also presented that is able to automatically exploit conditions of low vehicle uncertainty to more aggressively cover area in realtime. This results in faster progress towards the coverage goal than overly conservative planners that assume worst-case error at all times.
We further extend this to the multi-robot case where robots are able to communicate through a (possibly faulty) channel and make relative measurements of one another. In this case, area coverage can be achieved more quickly since the uncertainty of the robot trajectories will be reduced. We apply the framework to the scenario of mapping an area of seabed with a autonomous marine vehicles for minehunting purposes. The results show that the vehicles are able to achieve complete coverage with high confidence notwithstanding poor navigational sensors and resulting path-lengths are shorter than the worst-case planners.
Enabling robots for direct physical interaction and cooperation with humans and potentially unknown environments has been one of robotics research primary goals over decades. I will outline how our work on human-centered robot design, control, and planning may let robots for humans become a commodity in our near-future society. For this, we developed new generations of impedance controlled ultra-lightweight robots possibly equipped with Variable Impedance Actuation, previously at DLR, now in my new lab, which are sought to safely act as human assistants and collaborators at high performance over a variety of application domains. These may e.g. involve industrial assembly and manufacturing, medical assistance, or healthcare helpers in everyone's home, but also neurally controlled assistive devices. A recent generation of lightweight robots was commercialized as the KUKA LBR iiwa, which is considered to be the first commercial representative of this new class of robots. Based on a smart mechatronics design, a robot (let it be a manipulator, humanoid or flying system) has to be quipped with and also learn the skills than enable it to perceive and manipulate its' surrounding. Furthermore, it shall deduct according actions for successfully carrying out its given task, possibly in close collaboration with humans. At the same time the primary objective of a robot's action around humans is to ensure that even in case of malfunction or user errors no human shall be harmed, neither its surrounding be damaged. For this, instantaneous, truly human-safe, and intelligent context based force-sensitive controls and reactions to unforeseen events, partly inspired by the human motor control system, become crucial.
Bio: Sami Haddadin is Full Professor and Director of the Institute of Automatic Control (IRT) at Leibniz University Hanover (LUH), Germany. Until 2014 he was Scientific Coordinator "Terrestrial Assistance Systems" and "Human-Centered Robotics" at the DLR Robotics and Mechatronics Center. He was a visiting scholar at Stanford University in 2011 and a consulting scientist of Willow Garage, Inc., Palo Alto until 2013. He received degrees in Electrical Engineering (2006), Computer Science (2009), and Technology Management (2008) from TUM and LMU, respectively. He obtained his PhD with summa cum laude from RWTH Aachen in 2011. His research topics include physical Human-Robot Interaction, nonlinear robot control, real-time motion planning, real-time task and reflex planning, robot learning, optimal control, human motor control, variable impedance actuation, neuro-prosthetics, and safety in robotics. He was in the program/organization committee of several international robotics conferences and a guest editor of IJRR. He is an associate editor of the IEEE Transactions on Robotics. He published more than 100 scientific articles in international journals, conferences, and books. He received five best paper and video awards at ICRA/IROS, the 2008 Literati Best Paper Award, the euRobotics Technology Transfer Award 2011, and the 2012 George Giralt Award. He won the IEEE Transactions on Robotics King-Sun Fu Memorial Best Paper Award in 2011 and 2013. He is a recipient of the 2015 IEEE/RAS Early Career Award, the 2015 RSS Early Career Spotlight, the 2015 Alfried Krupp Award for Young Professors and was selected as 2015 Capital Young Elite Leader under 40 in Germany for the domain "Politics, State & Society".
Imagine a robot that could perceive and manipulate rigid objects as skillfully as a human adult. Would a robot that had such amazing capabilities be able to perform the range of practical manipulation tasks we expect in settings such as the home? Consider that this robot would still be unable to prepare a meal, do laundry, or make a bed because these tasks involve deformable object manipulation. Unlike in rigid-body manipulation, where methods exist for general-purpose pick-and-place tasks regardless of the size and shape of the object, no such methods exist for a similarly broad and practical class of deformable object manipulation tasks. The problem is indeed challenging, as these objects are not straightforward to model and have infinite-dimensional configuration spaces, making it difficult to apply established motion planning approaches. Our approach seeks to bypass these difficulties by representing deformable objects using simplified geometric models at both the global and local planning levels. Though we cannot predict the state of the object precisely, we can nevertheless perform tasks such as cable-routing, cloth folding, and surgical probe insertion in geometrically-complex environments. Building on this work, our new projects in this area aim to blend exploration of the model space with goal-directed manipulation of deformable objects and to generalize the methods we have developed to motion planning for soft robot arms, where we can exploit contact to mitigate the actuation uncertainty inherent in these systems.
Bio: Dmitry Berenson received a BS in Electrical Engineering from Cornell University in 2005 and received his Ph.D. degree from the Robotics Institute at Carnegie Mellon University in 2011, where he was supported by an Intel PhD Fellowship. He completed a post-doc at UC Berkeley in 2011 and started as an Assistant Professor in Robotics Engineering and Computer Science at WPI in 2012. He founded and directs the Autonomous Robotic Collaboration (ARC) Lab at WPI, which focuses on motion planning, manipulation, and human-robot collaboration.
I will present some recent work towards developing a "theory of co-design" that is rich enough to represent the trade-offs in the design of complex robotic systems, including the recursive constraints that involve energetics, propulsion, communication, computation, sensing, control, perception, and planning. I am developing a formalism in which "design problems" are the primitive objects, and multiple design problems can be composed to obtain "co-design" problems through operations analogous to series, parallel, and feedback composition. Certain monotonicity properties are preserved by these operations, from which it is possible to conclude existence and uniqueness of minimal feasible design trade-offs, as well as obtaining a systematic solution procedure. The mathematical tools used are the *really elementary* parts of the theory of fixed points on partially ordered sets (Kleene, Tarski, etc), of which no previous knowledge is assumed. We will conclude that: choosing the smallest battery for a drone, optimizing your controller to work over a network of limited bandwidth, and defining the semantics of programming languages, are one and the same problem.
End users expect appropriate robot actions, interventions, and requests for human assistance. As with most technologies, robots that behave in unexpected and inappropriate ways face misuse, abandonment, and sabotage. Complicating this challenge are human misperceptions of robot capability, intelligence, and performance. This talk will summarize work from several projects focused on this human-robot interaction challenge. Findings and examples will be shown from work on human trust in robots, deceptive robot behavior, robot motion, robot characteristics, and interaction with humans who are blind. I will also describe some lessons learned from related work in crowdsourcing (e.g., Tiramisu Transit) to help inform methods for enabling and supporting contributions by end users and local experts.
Bio: Aaron Steinfeld is an Associate Research Professor in the Robotics Institute (RI) at Carnegie Mellon University. He received his BSE, MSE, and Ph.D. degrees in Industrial and Operations Engineering from the University of Michigan and completed a Post Doc at U.C. Berkeley. He is the Co-Director of the Rehabilitation Engineering Research Center on Accessible Public Transportation (RERC-APT), Director of the DRRP on Inclusive Cloud and Web Computing, and the area lead for transportation related projects in the Quality of Life Technology Center (QoLT). His research focuses on operator assistance under constraints, i.e., how to enable timely and appropriate interaction when technology use is restricted through design, tasks, the environment, time pressures, and/or user abilities. His work includes intelligent transportation systems, crowdsourcing, human-robot interaction, rehabilitation, and universal design.
October 22 - David Held, Stanford University Using Motion to Understand Objects in the Real World (no video)
Many robots today are confined to operate in relatively simple, controlled
environments. One reason for this is that current methods for processing
visual data tend to break down when faced with occlusions, viewpoint
changes, poor lighting, and other challenging but common situations that
occur when robots are placed in the real world. I will show that we can
train robots to handle these variations by inferring the causes behind
visual appearance changes. If we model how the world changes over time, we
can be robust to the types of changes that objects often undergo. I
demonstrate this idea in the context of autonomous driving, and I show how
we can use this idea to improve performance on three different tasks:
velocity estimation, segmentation, and tracking with neural networks. By
inferring the causes of appearance changes over time, we can make our
methods more robust to a variety of challenging situations that commonly
occur in the real-world, thus enabling robots to come out of the factory
and into our lives.
Bio: David Held is a Computer Science Ph.D. student at Stanford working
with Sebastian Thrun and Silvio Savarese. He research interests include
robotics, vision, and machine learning, with applications to tracking and
object detection for autonomous driving. David has previously been a
researcher at the Weizmann Institute and has worked in industry as a
software developer. David has a Master's Degree in Computer Science from
Stanford and B.S. and M.S. degrees in Mechanical Engineering from MIT.
October 20 - Robotics Student/Faculty Mixer
October 7 - Matt Klingensmith, CMU Articulated SLAM (no video)
Uncertainty is a central problem in robotics. In order to understand and interact with the world, robots need to interpret signals from noisy sensors to reconstruct clear models not only of the world around them, but also their own internal state. For example, a mobile robot navigating an unknown space must simultaneously reconstruct a model of the world around it, and localize itself against that model using noisy sensor data from wheel odometry, lasers, cameras, or other sensors. This problem (called the SLAM problem) is very well-studied in the domain of mobile robots. Less well-studied is the equivalent problem for robot manipulators. That is, given a multi-jointed robot arm with a noisy hand-mounted sensor, how can the robot simultaneously estimate its state and generate a coherent 3D model of the world? We call this the articulated SLAM problem.
Given actuator uncertainty and sensor uncertainty, what is the correct way of simultaneously constructing a model of the world and estimating the robot's state? In this work, we show that certain contemporary visual SLAM techniques can be mapped to the articulated SLAM problem by using the robot's joint configuration space as the state space for localization, rather than the typical SE(3). We map one kind of visual SLAMt technique, Kinect Fusion, to the robot's configuration space, and show how the robot's joint encoders can be used appropriately to inform the pose of the camera. The idea that the configuration of the robot is not merely a sensor which informs the pose of the camera, but rather it is the underlying latent state of the system is critical to our analysis. Tracking the configuration of the robot directly allows us to build algorithms on top of the SLAM system which depend on knowledge of the joint angles (such as motion planners and control systems).
May 12 - Dieter Fox, UW RGB-D Perception in Robotics
RGB-D cameras provide per pixel color and depth information at high frame rate and resolution. Gaming and entertainment applications such as the Microsoft Kinect system resulted in the mass production of RGB-D cameras at extremely low cost, also making them available for a wide range of robotics applications. In this talk, I will provide an overview of depth camera research done in the Robotics and State Estimation Lab over the last six years. This work includes 3D mapping of static and dynamic scenes, autonomous object modeling and recognition, and articulated object tracking.
Bio: Dieter Fox is a Professor in the Department of Computer Science & Engineering at the University of Washington, where he heads the UW Robotics and State Estimation Lab. From 2009 to 2011, he was also Director of the Intel Research Labs Seattle. He currently serves as the academic PI of the Intel Science and Technology Center for Pervasive Computing hosted at UW. Dieter obtained his Ph.D. from the University of Bonn, Germany. Before going to UW, he spent two years as a postdoctoral researcher at the CMU Robot Learning Lab. Fox's research is in artificial intelligence, with a focus on state estimation applied to robotics and activity recognition. He has published over 150 technical papers and is co-author of the text book "Probabilistic Robotics". He is an IEEE and a AAAI fellow, and received several best paper awards at major robotics and AI conferences. He is an editor of the IEEE Transactions on Robotics, was program co-chair of the 2008 AAAI Conference on Artificial Intelligence, and served as the program chair of the 2013 Robotics: Science and Systems conference.
On June 5-6 of this year, 25 of the most advanced robots in the world will descend on Pomona, California to compete in the final DARPA Robotics Challenge competition (http://theroboticschallenge.org). Each of these robots will be sent into a disaster response situation to perform complex locomotion and manipulation tasks with limited power and comms. Team MIT is one of only 2 academic teams that has survived all of the qualifying rounds, and we are working incredibly hard to showcase the power of our relatively formal approaches to perception, estimation, planning, and control.
In this talk, I’ll dig into a number of technical research nuggets that have come to fruition during this effort, including an optimization-based planning and control method for robust and agile online gait and manipulation planning, efficient mixed-integer optimization for negotiating rough terrain, convex relaxations for grasp optimization, powerful real-time perception systems, and essentially drift-free state estimation. I’ll discuss the formal and practical challenges of fielding these on a very complex (36+ degree of freedom) humanoid robot that absolutely has to work on game day.
Apr 28 - Ioannis Poulakakis, University of Delaware Legged Robots Across Scales: Integrating Motion Planning and Control through Canonical Locomotion Models
Abstract: On a macroscopic level, legged locomotion can be understood through reductive canonical models -- often termed templates -- the purpose of which is to capture the dominant features of an observed locomotion behavior without delving into the fine details of a robot’s (or animal’s) structure and morphology. Such models offer unifying, platform-independent, descriptions of task-level behaviors, and inform control design for legged robots. This talk will discuss reductive locomotion models for diverse legged robots, ranging from slow-moving, palm-size, eight-legged crawlers to larger bipeds and quadrupeds, and will focus on the role of such models in integrating locomotion control and motion planning within a unifying framework that translates task-level specifications to suitable low-level control actions that harness the locomotion capabilities of the robot platforms.
Bio: Prof. Poulakakis earned his Ph.D. in Electrical Engineering from the University of Michigan in 2008, served as a postdoctoral research associate at Princeton University for two years, and then joined the Department of Mechanical Engineering at the University of Delaware in 2010 as an Assistant Professor. His research interests are in the area of dynamics and control with application to bio-inspired robotic systems, specifically legged robots. In 2014 he received a Faculty Early Career Development Award from the National Science Foundation to investigate task planning and motion control for legged robots at different scales.
Apr 21 - No seminar - MIT MONDAY SCHEDULE (due to Patriots Day)
Apr 14 - Ted Adelson, MIT GelSight sensors for high resolution touch sensing in robotics, and many other things
GelSight is a technology for high resolution touch sensing, which has a wide range of applications, some unexpected. A sensor consists of a slab of clear elastomer covered with a reflective membrane, along with an embedded camera and light system. The goal was to build a robot fingertip that could match the softness and sensitivity of human skin. Using machine vision (mainly photometric stereo) one can touch a surface and quickly derive high resolution 3D geometry, allowing estimates of shape, texture, and force. By adding internal markers one can estimate tangential interactions (friction, shear and slip). With collaborators we are learning how to use this information in robotic manipulation and surface sensing. GelSight’s extraordinarily high resolution has also led to a spin-off company, GelSight Inc., which makes instruments that measure the micron scale 3D geometry. Variants are being used commercially to support 3D printing, to enable forensics on bullet casings, to study human skin, and (in a large version) to measure feet for custom insoles.
Apr 7 - Allison Okamura, Stanford University Department of Mechanical Engineering Modeling, Planning, and Control for Robot-Assisted Medical Interventions
Abstract: Many medical interventions today are qualitatively and quantitatively limited by human physical and cognitive capabilities. This talk will discuss several robot-assisted intervention techniques that will extend humans' ability to carry out interventions more accurately and less invasively. First, I will describe the development of minimally invasive systems that deliver therapy by steering needles through deformable tissue and around internal obstacles to reach specified targets. Second, I will review recent results in haptic (touch) feedback for robot-assisted teleoperated surgery, in particular the display of tissue mechanical properties. Finally, I will demonstrate the use of dynamic models of the body to drive novel rehabilitation strategies. All of these systems incorporate one or more key elements of robotic interventions: (1) quantitative descriptions of patient state, (2) the use of models to plan interventions, (3) the design of devices and control systems that connect information to physical action, and (4) the inclusion of human input in a natural way.
Biosketch: Allison M. Okamura received the BS degree from the University of California at Berkeley in 1994, and the MS and PhD degrees from Stanford University in 1996 and 2000, respectively, all in mechanical engineering. She is currently an Associate Professor in the mechanical engineering department at Stanford University, with a courtesy appointment in Computer Science. She is Editor-in-Chief of the IEEE International Conference on Robotics and Automation and an IEEE Fellow. Her academic interests include haptics, teleoperation, virtual and augmented reality, medical robotics, neuromechanics and rehabilitation, prosthetics, and engineering education. Outside academia, she enjoys spending time with her husband and two children, running, and playing ice hockey. For more information about her research, please see the Collaborative Haptics and Robotics in Medicine (CHARM) Laboratory website: http://charm.stanford.edu.
Mar 31 - student/faculty mixer
Mar 24 - MIT SPRING VACATION Special : Frank Dellaert, Georgia Tech Factor Graphs for Flexible Inference in Robotics and Vision
Abstract: Simultaneous Localization and Mapping (SLAM) and Structure from Motion (SFM) are important and closely related problems in robotics and vision. I will show how both SLAM and SFM instances can be posed in terms of a graphical model, a factor graph, and that inference in these graphs can be understood as variable elimination. The overarching theme of the talk will be to emphasize the advantages and intuition that come with seeing these problems in terms of graphical models. For example, while the graphical model perspective is completely general, linearizing the non-linear factors and assuming Gaussian noise yields the familiar direct linear solvers such as Cholesky and QR factorization. Based on these insights, we have developed both batch and incremental algorithms defined on graphs in the SLAM/SFM domain. I will also discuss my recent work on using polynomial bases for trajectory optimization, inspired by pseudospectral optimal control, which is made easy by the new Expressions language in GTSAM 4, currently under development.
Bio: Frank Dellaert is currently on leave from the Georgia Institute of Technology for a stint as Chief Scientist of Skydio, a startup founded by MIT grads to create intuitive interfaces for micro-aerial vehicles. When not on leave, he is a Professor in the School of Interactive Computing and Director of the Robotics PhD program at Georgia Tech. His research interests lie in the overlap of Robotics and Computer vision, and he is particularly interested in graphical model techniques to solve large-scale problems in mapping and 3D reconstruction. You can find out about his group’s research and publications at https://borg.cc.gatech.edu and http://www.cc.gatech.edu/~dellaert. The GTSAM toolbox which embodies many of the ideas his group has worked on in the past few years is available for download at http://tinyurl.com/gtsam. But really hardcore users can ask to be plugged into our BitBucket motherlode. Just send mail to firstname.lastname@example.org.
Mar 17 - Leslie Pack Kaelbling, MIT CSAIL Making Robots Behave
The fields of AI and robotics have made great improvements in many individual subfields, including in motion planning, symbolic planning, probabilistic reasoning, perception, and learning. Our goal is to develop an integrated approach to solving very large problems that are hopelessly intractable to solve optimally. We make a number of approximations during planning, including serializing subtasks, factoring distributions, and determinizing stochastic dynamics, but regain robustness and effectiveness through a continuous state-estimation and replanning process. This approach is demonstrated in three robotic domains, each of which integrates perception, estimation, planning, and manipulation.
Mar 10 - Hanu Singh, Woods Hole Oceanographic Institute Bipolar Robotics: Exploring the Arctic and the Antarctic with a stop for some Coral Reef Ecology in the Middle
The Arctic and Antarctic remain one of least explored parts of the world's oceans. This talk looks at efforts over the last decade to explore areas under-ice which have traditionally been difficult to access. The focus of the talk will be on the robots, the role of communications over low bandwidth acoustic links, navigation and imaging and mapping methodologies. This issues will all be discussed within the context of real data collected on several expeditions to the Arctic and Antarctic.
Mar 3 - Brandon Basso, UC Berkeley The 3D Robotics Open UAV Platform
3D Robotics is a venture-backed aerospace startup in Berkeley, California. At the heart of our platform is the the Pixhawk autopilot which runs on more UAVs in the world than any other autopilot and represents the worlds largest open source robotics project, Ardupilot. This talk will explore the technological advancements that have enabled an entirely open and viral UAV platform, from low-level estimation to high-level system architecture. Two recent advancements will be explored in detail: Efficient algorithms for state estimation using low-cost IMUs, and cloud-based architecture for real-time uplink and downlink from any internet-connected vehicle. Some concluding thoughts on future platform evolution and the growing consumer drone space will be presented.
Feb 24 - Russell H. Taylor, The Johns Hopkins University Medical Robotics and Computer-Integrated Interventional Medicine
Computer-integrated interventional systems (CIIS) combine innovative algorithms, robotic devices, imaging systems, sensors, and human-machine interfaces to work cooperatively with surgeons in the planning and execution of surgery and other interventional procedures. The impact of CIIS on medicine in the next 20 years will be as great as that of Computer-Integrated Manufacturing on industrial production over the past 20 years. A novel partnership between human surgeons and machines, made possible by advances in computing and engineering technology, will overcome many of the limitations of traditional surgery. By extending human surgeons’ ability to plan and carry out surgical interventions more accurately and less invasively, CIIS systems will address a vital need to greatly reduce costs, improve clinical outcomes, and improve the efficiency of health care delivery.
This talk will describe past and emerging research themes in CIIS systems and illustrate them with examples drawn from our current research activities within Johns Hopkins University’s Engineering Research Center for Computer Integrated Surgical systems and Technology
Russell H. Taylor received his Ph.D. in Computer Science from Stanford in 1976. He joined IBM Research in 1976, where he developed the AML robot language and managed the Automation Technology Department and (later) the Computer-Assisted Surgery Group before moving in 1995 to Johns Hopkins, where he is the John C. Malone Professor of Computer Science with joint appointments in Mechanical Engineering, Radiology, and Surgery and is also Director of the Engineering Research Center for Computer-Integrated Surgical Systems and Technology (CISST ERC) and of the Laboratory for Computational Sensing and Robotics (LCSR). He is the author of over 375 peer-reviewed publications, a Fellow of the IEEE, of the AIMBE, of the MICCAI Society, and of the Engineering School of the University of Tokyo. He is also a recipient of numerous awards, including the IEEE Robotics Pioneer Award, the MICCAI Society Enduring Impact Award, and the Maurice Müller Award for Excellence in Computer-Assisted Orthopaedic Surgery.
Fall 2014 Campus-wide Robotics Seminar
Dec 9 - Tim Bretl, U Illinois Urbana-Champaign Mechanics, Manipulation, and Perception of an Elastic Rod (video)
Abstract: This talk is about robotic manipulation of canonical "deformable linear objects" like a Kirchhoff elastic rod (e.g., a flexible wire). I continue to be amazed by how much can be gained by looking carefully at the mechanics of these objects and at the underlying mathematics. For example, did you know that the free configuration space of an elastic rod is path-connected? I'll prove it, and tell you why it matters.
Bio: Timothy Bretl comes from the University of Illinois at Urbana-Champaign, where he is an Associate Professor of Aerospace Engineering and of the Coordinated Science Laboratory.
Dec 2 - Steve LaValle, Professor, University of Illinois & Principal Scientist, Oculus/Facebook Robotics Meets Virtual Reality (video)
Abstract: Roboticists are well positioned to strongly impact the rising field of virtual reality (VR). Using the latest technology, we can safely take control of your most trusted senses, thereby fooling your brain into believing you are in another world. VR has been around for a long time, but due to the recent convergence of sensing, display, and computation technologies, there is an unprecedented opportunity to explore this form of human augmentation with lightweight, low-cost materials and simple software platforms. Many of the issues are familiar to roboticists, such as position and orientation tracking from sensor data, maintaining features from vision data, and dynamical system modeling. In addition, there is an intense form of human-computer interaction (HCI) that requires re-examining core engineering principles with a direct infusion of perceptual psychology research. With the rapid rise in consumer VR, fundamental research questions are popping up everywhere, slicing across numerous disciplines from engineering to sociology to film to medicine. This talk will provide some perspective on where we have been and how roboticists can help participate in this exciting future!
Bio: Steve LaValle started working with Oculus VR in September 2012, a few days after their successful Kickstarter campaign, and was the head scientist up until the Facebook acquisition in March 2014. He developed perceptually tuned head tracking methods based on IMUs and computer vision. He also led a team of perceptual psychologists to provide principled approaches to virtual reality system calibration and the design of comfortable user experiences. In addition to his continuing work at Oculus, he is also Professor of Computer Science at the University of Illinois, where he joined in 2001. He has worked in robotics for over 20 years and is known for his introduction of the Rapidly exploring Random Tree (RRT) algorithm of motion planning and his 2006 book, Planning Algorithms.
Nov 25 -
Richard Newcombe, University of Washington Andrea Censi, MIT LIDS
Robotics video session : screening/voting session for the ICRA 2015 trailer
Nov 18 - Sachin Patil, UC Berkeley Coping with Uncertainty in Robotic Navigation, Exploration, and Grasping
A key challenge in robotics is to robustly complete navigation, exploration, and manipulation tasks when the state of the world is uncertain. This is a fundamental problem in several application areas such as logistics, personal robotics, and healthcare where robots with imprecise actuation and sensing are being deployed in unstructured environments. In such a setting, it is necessary to reason about the acquisition of perceptual knowledge and to perform information gathering actions as necessary. In this talk, I will present an approach to motion planning under motion and sensing uncertainty called "belief space" planning where the objective is to trade off exploration (gathering information) and exploitation (performing actions) in the context of performing a task. In particular, I will present how we can use trajectory optimization to compute locally-optimal solutions to a determinized version of this problem in Gaussian belief spaces. I will show that it is possible to obtain significant computational speedups without explicitly optimizing over the covariances by considering a partial collocation approach. I will also address the problem of computing such trajectories, given that measurements may not be obtained during execution due to factors such as limited field of view of sensors and occlusions. I will demonstrate this approach in the context of robotic grasping in unknown environments where the robot has to simultaneously explore the environment and grasp occluded objects whose geometry and positions are initially unknown.
Nov 4 - Mark Cutkosky, Stanford Bio-Inspired Dynamic Surface Grasping (video)
The adhesive system of the gecko has several remarkable properties that make it ideal for agility on vertical and overhanging surfaces. It requires very little preload for sticking, and (unlike sticky tape) very little effort to detach. It resists fouling when the gecko travels over dusty surfaces, and it is controllable: the amount of adhesion in the normal direction depends on the applied tangential force. Moreover, it is fast, allowing the gecko to climb at speeds of a meter per second. The desirable properties of the gecko's adhesive apparatus are a result of its unique, hierarchical structure, with feature sizes ranging from hundreds of nanometers to millimeters. Over the last several years, analogous features have been incorporated into various synthetic gecko-inspired adhesives, with gradually improving performance from the standpoints of adhesion, ease and speed of attachment and detachment, etc. In this talk we will explore recent developments to scale gecko-inspired directional adhesives beyond small wall-climbing robots to new applications including perching quadrotors and grappling space debris in orbit. These applications require scaling the adhesives to areas of 10x10cm or larger on flat or curved surfaces without loss in performance, and attachment in milliseconds to prevent bouncing. The solutions draw some inspiration from the arrangement of tendons and other compliant structures in the gecko's toe.
Oct 28 - Robotics student/faculty mixer
Oct 21 - Anca Dragan, Carnegie Mellon Interaction as Manipulation (video)
The goal of my research is to enable robots to autonomously produce behavior that reasons about function _and_ interaction with and around people. I aim to develop a formal understanding of interaction that leads to algorithms which are informed by mathematical models of how people interact with robots, enabling generalization across robot morphologies and interaction modalities.
In this talk, I will focus on one specific instance of this agenda: autonomously generating motion for coordination during human-robot collaborative manipulation. Most motion in robotics is purely functional: industrial robots move to package parts, vacuuming robots move to suck dust, and personal robots move to clean up a dirty table. This type of motion is ideal when the robot is performing a task in isolation. Collaboration, however, does not happen in isolation, and demands that we move beyond purely functional motion. In collaboration, the robot's motion has an observer, watching and interpreting the motion – inferring the robot's intent from the motion, and anticipating the robot's motion based on its intent. My work develops a mathematical model of these inferences, and integrates this model into motion planning, so that the robot can generate motion that matches people's expectations and clearly conveys its intent. In doing so, I draw on action interpretation theory, Bayesian inference, constrained trajectory optimization, and interactive learning. The resulting motion not only leads to more efficient collaboration, but also increases the fluency of the interaction as defined through both objective and subjective measures. The underlying formalism has been applied across robot morphologies, from manipulator arms to mobile robots, and across interaction modalities, such as motion, gestures, and shared autonomy with assistive arms.
Oct 14 - Sangbae Kim, MIT The actuation and the control of the MIT Cheetah (video)
Biological machines created by millions of years of evolution suggest a paradigm shift in robotic design. Realizing animals’ magnificent locomotive capabilities is next big challenge in mobile robotic applications. The main theme of MIT Biomimetic Robotics Laboratory is innovation through ‘principle extraction’ from biology. The embodiment of such innovations includes Stickybot that employs the world’s first synthetic directional dry adhesive inspired by geckos, and the MIT Cheetah, designed after the fastest land animal. The design principles in structures, actuation and control algorithms applied in the MIT Cheetah will be presented during the talk. The Kim’s creations are opening new frontiers in robotics and leading to advanced mobile robots that can save lives in dangerous situations, and new all-around robotic transportation systems for the mobility-impaired.
Oct 7 - Nick Roy, MIT Project Wing: Self-flying vehicles for Package Delivery
Autonomous UAVs, or "self-flying vehicles", hold the promise of transforming a number of industries, and changing how we move things around the world. Building from the foundation of decades of research in autonomy and UAVs, Google launched Project Wing in 2012 and recently announced trials of a delivery service using a small fleet of autonomous UAVs in Australia. In this talk, I will provide an introduction to the work Google has been doing in developing this service, describe the capabilities (and limitations) of the vehicles, and talk briefly about the promise of UAVs in general.