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Robot Locomotion Group




    The goal of our research is to build machines which exploit their natural dynamics to achieve extraordinary agility and efficiency. In an age where "big data" is all the rage, we still have relatively limited data from robots in these regimes, and instead rely mostly on existing models (e.g. from Lagrangian mechanics) and model-based optimization. We believe that deep connections are possible -- enabling very efficient optimization by exploiting structure in the governing equations -- and are working hard on both optimization algorithms and control applications. Our current projects include dynamics and control for humanoid robots, robotic manipulation, and dynamic walking over rough terrain, flight control for aggressive maneuvers in unmanned aerial vehicles, feedback control for fluid dynamics and soft robotics, and connections between perception and control.

    We are currently participating in the DARPA Robotics Challenge. Make sure you check out our videos here.

    The Robot Locomotion Group is a part of the CSAIL Center for Robotics.

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Locomotion Group Paper and Multimedia News  

    Localizing External Contact Using Proprioceptive Sensors: The Contact Particle Filter
      by Lucas Manuelli and Russ Tedrake

      In order for robots to interact safely and intelligently with their environment they must be able to reliably estimate and localize external contacts. This paper introduces CPF, the Contact Particle Filter, which is a general algorithm for detecting and localizing external contacts on rigid body robots without the need for external sensing. CPF finds external contact points that best explain the observed external joint torque, and returns sensible estimates even when the external torque measurement is corrupted with noise. We demonstrate the capability of the CPF to track multiple external contacts on a simulated Atlas robot, and compare our work to existing approaches.

      Supplemental materials: http://youtu.be/ckvsMK0QhB0

      Under review. Comments welcome.

    Funnel Libraries for Real-time Robust Feedback Motion Planning

      by Anirudha Majumdar and Russ Tedrake

      In this paper we consider the problem of generating motion plans for a robot that are guaranteed to succeed despite uncertainty in the environment, parametric model uncertainty, and disturbances. Furthermore, we consider the case where these plans must be generated in real- time, because constraints such as obstacles in the environment may not be known until they are perceived (with a noisy sensor) at runtime. Previous work on feedback motion planning for nonlinear systems was limited to offline planning due to the computational cost of safety verification. Here we augment the traditional trajectory library approach by designing locally stabilizing controllers for each nominal trajectory in the library and providing guarantees on the resulting closed-loop systems. We leverage sums-of-squares (SOS) programming to design these locally stabilizing controllers by explicitly attempting to minimize the size of the worst case reachable set of the closed-loop system subjected to bounded disturbances and uncertainty. The reachable sets associated with each trajectory in the library can be thought of as funnels that the system is guaranteed to remain within. The resulting funnel library is then used to sequentially compose motion plans at runtime while ensuring the safety of the robot. A major advantage of the work presented here is that by explicitly taking into account the effect of uncertainty, the robot can evaluate motion plans based on how vulnerable they are to disturbances. We demonstrate our method using thorough simulation experiments of a ground vehicle model navigating through cluttered environments and also present extensive hardware experiments validating the approach on a small fixed-wing airplane avoiding obstacles at high speed.

      Preliminary version on arxiv. Comments welcome.

    Director: A User Interface Designed for Robot Operation With Shared Autonomy

      by Pat Marion and Robin Deits and Andr\'{e}s Valenzuela and Claudia P\'{e}rez D'Arpino and Greg Izatt and Lucas Manuelli and Matt Antone and Hongkai Dai and Twan Koolen and John Carter and Maurice Fallon and Scott Kuindersma and and Russ Tedrake

      Operating a high degree of freedom mobile manipulator, such as a humanoid, in a field scenario requires constant situational awareness, capable perception modules, and effective mechanisms for interactive motion planning and control. A well-designed operator interface presents the operator with enough context to quickly carry out a mission and the flexibility to handle unforeseen operating scenarios robustly. By contrast, an unintuitive user interface can increase the risk of catastrophic operator error by overwhelming the user with unnecessary information. With these principles in mind, we present the philosophy and design decisions behind Director -- the open-source user interface developed by Team MIT to pilot the Atlas robot in the DARPA Robotics Challenge (DRC). At the heart of Director is an integrated task execution system that specifies sequences of actions needed to achieve a substantive task, such as drilling a wall or climbing a staircase. These task sequences, developed a priori, make queries to automated perception and planning algorithms online with outputs that can be reviewed by the operator and executed by our whole-body controller. Our use of Director at the DRC resulted in efficient high-level task operation while being fully competitive with approaches focusing on teleoperation by highly-trained operators. We discuss the primary interface elements that comprise the Director and provide analysis of its successful use at the DRC.

      Under review. Comments welcome.

    Feedback-Motion-Planning with Simulation-Based LQR-Trees

      by Philipp Reist and Pascal V. Preiswerk and Russ Tedrake

      The paper presents the simulation-based variant of the LQR-Tree feedback-motion-planning approach. The algorithm generates a control policy that stabilizes a nonlinear dynamic system from a bounded set of initial conditions to a goal. The policy is represented by a tree of feedback-stabilized trajectories. The algorithm explores the bounded set with random state samples and, where needed, adds new trajectories to the tree using motion planning. Simultaneously, the algorithm approximates the funnel of a trajectory, which is the set of states that can be stabilized to the goal by the trajectory’s feedback policy. Generating a control policy that stabilizes the bounded set to the goal is equivalent to adding trajectories to the tree until their funnels cover the set. In previous work, funnels are approximated with sums-of-squares verification. Here, funnels are approximated by sampling and falsification by simulation, which allows the application to a broader range of systems and a straightforward enforcement of input and state constraints. A theoretical analysis shows that in the long run, the algorithm tends to improve the coverage of the bounded set as well as the funnel approximations. Focusing on the practical application of the method, a detailed example implementation is given that is used to generate policies for two example systems. Simulation results support the theoretical findings, while experiments demonstrate the algorithm’s state-constraints capability, and applicability to highly-dynamic systems.

      Supplemental materials: http://youtu.be/0-iHS6QdDuM

      To appear in the IJRR.

    High-Speed Autonomous Obstacle Avoidance with Pushbroom Stereo

      by Andrew J. Barry and Russ Tedrake

      We present the design and implementation of a small autonomous unmanned aerial vehicle capable of high-speed flight through complex natural environments. Using only onboard sensing and computation, we perform obstacle detection, planning, and feedback control in realtime. We introduce a novel stereo vision algorithm, pushbroom stereo, capable of detecting obstacles at 120 frames per second without overburdening our lightweight processors. Our use of model-based planning and control techniques allows us to track precise trajectories that avoid obstacles identified by the vision system. We demonstrate a complete working system avoiding trees at up to 14 m/s (31 MPH). To the best of our knowledge this is the fastest lightweight aerial vehicle to perform collision avoidance in such a complex environment.

      Under review. Comments welcome.


Locomotion Group News  

    February 24, 2016. Media. NOVA's documentary on the DARPA Robotics Challenge, titled "Rise of the Robots" is online now.

    December 7, 2015. PhD Defense. Andy Barry has successfully defended his PhD thesis. Congratulations Andy! Click on the link to watch his talk.

    November 18, 2015. In the news. NASA's R5 humanoid robot is coming to MIT. We're very excited to have the opportunity to do research on this amazing platform.

    November 5, 2015. Award. Our DRC Team's continuous walking with stereo fusion paper just won the Best Paper Award (Oral) at Humanoids 2015. Congratulations all!

    November 5, 2015. In the news. Andy's video of high-speed UAV obstacle avoidance (using only onboard processing) got some great coverage this week. This article by the IEEE Spectrum was particularly nice and insightful.

    October 26, 2015. PhD Defense. Andres Valenzuela just successfully defended his PhD thesis. Congratulations Andres!

    May 29, 2015. News. We're heading off to the DARPA Robotics Challenge. We've been posting some fun videos to our YouTube site (linked here). Wish us luck!

    May 26, 2015. News. Benoit Landry has submitted his Masters Thesis on Aggressive Quadrotor Flight in Dense Clutter. Be sure to check out his cool video.

    May 28, 2015. News. Scott Kuindersma has accepted a tenure-track position at Harvard starting this fall. Congratulations Scott!

    November 20, 2014. Award. Robin Deits' paper on Mixed-integer optimization for footstep planning just won the Best Paper Award (Oral) at Humanoids 2014. Congratulations Robin!

    December 31, 1969. Award. Benoit Landry has been awarded the 2014 Siebel Scholarship. Congratulations Benoit!

    June 18, 2014. News. Ram Vasudevan has officially accepted a tenure-track position at the University of Michigan, Ann Arbor. Congratulations Ram! Go Blue!

    June 16, 2014. News. Joe Moore has officially accepted a position at the Johns Hopkins Applied Phsyics Lab. Congratulations Joe!

    May 27, 2014. PhD Defense. Joseph Moore officially defended his PhD. You can watch his defense on the group talks page. Congratulations Joe!

    December 21, 2013. News. Team MIT advances to the next round in the DARPA Robotics Challenge.

    October 2, 2013. Award. Michael Posa has been awarded the 2013 Rolf Locher Graduate Fellowship. Congratulations Michael!

    September 14, 2013. News. The Robot Locomotion Group hosted the evening session of the 2014 Boston Barefoot Running Festival.

    September 11, 2013. In the News. Tough robo-challenge casts robots as rescuers.

    June 27, 2013. In the News. Team MIT Completes First Hurdle in DARPA Robotics Challenge.

    May 9, 2013. Award. Ani Majumdar and Amir Ali Ahmadi's paper on nonlinear control design along trajectories just won the Best Paper Award at ICRA 2013. Congratulations!

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