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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, efficiency, and robustness using rigorous tools from dynamical systems, control theory, and machine learning. Our current focus in on robotic manipulation, because the revolution in recent machine learning has opened a pathway in these applications to merging control theory and perception at a level that has never been considered before; ideas like "intuitive physics" and "common-sense reasoning" will meet with rigorous ideas like "model-order reduction" and "robust/adaptive control". It's going to be a great few years!
Our previous projects have included dynamics and control for humanoid robots, 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.
The Robot Locomotion Group is a part of Robotics @ MIT and CSAIL.
Locomotion Group Paper and Multimedia News 
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Learning Models as Functionals of Signed-Distance Fields for Manipulation Planning
- by Danny Driess and Jung-Su Ha and Marc Toussaint and Russ Tedrake
This work proposes an optimization-based manipulation planning framework where the objectives are learned functionals of signed-distance fields that represent objects in the scene. Most manipulation planning approaches rely on analytical models and carefully chosen abstractions/state-spaces to be effective. A central question is how models can be obtained from data that are not primarily accurate in their predictions, but, more importantly, enable efficient reasoning within a planning framework, while at the same time being closely coupled to perception spaces. We show that representing objects as signed-distance fields not only enables to learn and represent a variety of models with higher accuracy compared to point-cloud and occupancy measure representations, but also that SDF-based models are suitable for optimization-based planning. To demonstrate the versatility of our approach, we learn both kinematic and dynamic models to solve tasks that involve hanging mugs on hooks and pushing objects on a table. We can unify these quite different tasks within one framework, since SDFs are the common object representation. Video: https://youtu.be/ga8Wlkss7co
Supplemental materials: https://www.youtube.com/watch?v=ga8Wlkss7co
Recently presented at CoRL:2021
Discrete Approximate Information States in Partially Observable Environments
- by Lujie Yang and Kaiqing Zhang and Alexandre Amice and Yunzhu Li and Russ Tedrake
The notion of approximate information states (AIS) was introduced in as a methodology for learning task- relevant state representations for control in partially observable systems. They proposed particular learning objectives which attempt to reconstruct the cost and next state and provide a bound on the suboptimality of the closed-loop performance, but it is unclear whether these bounds are tight or actually lead to good performance in practice. Here we study this methodology by examining the special case of discrete approximate information states (DAIS). In this setting, we can solve for the globally optimal policy using value iteration, allowing us to disambiguate the performance of the AIS objective from the policy search. Going further, for small problems with finite information states, we reformulate the DAIS learning problem as a novel mixed-integer program (MIP) and solve it to its global optimum; in the infinite information states case, we introduce clustering-based and end-to-end gradient-based optimization methods for minimizing the DAIS construction loss. We study DAIS in three partially observable environments and find that the AIS objective offers relatively loose bounds for guaranteeing monotonic performance improvement and is sufficient but not necessary for implementing optimal controllers. DAIS may even prove useful in practice by itself or as part of mixed discrete- and continuous-state representations, due to its ability to represent logical state, to its potential interpretabilty, and to the availability of these stronger algorithms.
Under review. Comments welcome.
{SEED}: Series Elastic End Effectors in 6D for Visuotactile Tool Use
- by H.J. Terry Suh and Naveen Kuppuswamy and Tao Pang and Paul Mitiguy and Alex Alspach and Russ Tedrake
We propose the framework of Series Elastic End Effectors in 6D (SEED), which combines a spatially compliant element with visuotactile sensing to grasp and manipulate tools in the wild. Our framework generalizes the benefits of series elasticity to 6-dof, while providing an abstraction of control using visuotactile sensing. We propose an algorithm for relative pose estimation from visuotactile sensing, and a spatial hybrid force-position controller capable of achieving stable force interaction with the environment. We demonstrate the effectiveness of our framework on tools that require regulation of spatial forces. Video link: https://youtu.be/2-YuIfspDrk
Supplemental materials: https://arxiv.org/abs/2111.01376 , https://youtu.be/2-YuIfspDrk
Under review. Comments welcome.
Scene Understanding and Distribution Modeling with Mixed-Integer Scene Parsing
- by Izatt, Gregory and Tedrake, Russ
Under review. Comments welcome.
Easing Reliance on Collision-free Planning with Contact-aware Control
- by Tao Pang and Russ Tedrake
We believe that the future of robot motion planning will look very different than how it looks today: instead of complex collision avoidance trajectories with a brittle dependence on sensing and estimation of the environment, motion plans should consist of smooth, simple trajectories and be executed by robots that are not afraid of making contact. Here we present a ``contact-aware
Under review. Comments welcome.
Locomotion Group News
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August 15, 2020. Talks on Zoom. For better or worse, more of our talks are now online. I've posted a handful of links to new talks, including Russ on Lex Fridman's AI Podcast, and at the IFRR Colloquium on the Roles of Physics-Based Models and Data-Driven Learning in Robotics.
July 20, 2020. PhD Defense. Congratulations to Lucas Manuelli for successfully defending his PhD thesis!
May 29, 2020. PhD Defense. Congratulations to Shen Shen for successfully defending her thesis!
September 18, 2019. PhD Defense. Congratulations to Twan Koolen for successfully defending his thesis!
August 19, 2019. PhD Defense. Congratulations to Pete Florence for successfully defending his thesis!
October 15, 2018. PhD Defense. Congratulations to Robin Deits for successfully defending his thesis!
October 3, 2018. Award. Congratulations to Pete Florence and Lucas Manuelli whose paper Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation won the Conference Best Paper Award at CoRL 2018!
September 19, 2018. Award. Congratulations to Pete Florence and Lucas Manuelli whose paper Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation won the first ever Amazon Robotics Best Technical Paper Award (2018).
June 18, 2018. Award. Congratulations to Ani Majumdar whose paper Funnel libraries for real-time robust feedback motion planning won the first ever International Journal of Robotics Research Paper of the Year (2017).
April 26, 2018. Award. Congratulations to Katy Muhlrad for winning the "Audience Choice Award" at the SuperUROP Showcase for her work on "Using GelSight to Identify Objects by Touch".
July 26, 2017. Defense. Frank Permenter successfully defended his thesis, titled "Reduction methods in semidefinite and conic optimization". Congratulations Frank!
May 19, 2017. Award. Pete Florence was awarded the EECS Masterworks award. Congratulations Pete!
May 19, 2017. Award. Sarah Hensley was awarded the 2017 Best SuperUROP Presentation award. Congratulations Sarah!
May 16, 2017. PhD Defense. Michael Posa successfully defended his thesis, titled "Optimization for Control and Planning of Multi-Contact Dynamic Motion". Congratulations Michael!
May 15, 2017. Award. Our paper describing the planning and control that we implemented on Atlas for the DARPA Robotics Challenge was recognized with the IEEE-RAS Technical Commmittee on Whole-Body Control 2016 Best Paper of the Year award.
January 28, 2017. Video. Amara Mesnik put together a great mini-documentary on MIT's entry in the DARPA Robotics Challenge.
May 13, 2016. PhD Defense. Ani Majumdar has successfully defended his PhD thesis. Congratulations Ani! Click on the link to watch his talk, and check the publications page to read his thesis.
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.
