Home
People
Publications
Talks/Lectures
Video Highlights
Robotics Challenge
Software
Directions
Accessibility
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 
-
Sampling-Based Motion Planning with Discrete Configuration-Space Symmetries
- by Thomas Cohn and Russ Tedrake
When planning motions in a configuration space that has underlying symmetries (e.g. when manipulating one or multiple symmetric objects), the ideal planning algorithm should take advantage of those symmetries to produce shorter trajectories. However, finite symmetries lead to complicated changes to the underlying topology of configuration space, preventing the use of standard algorithms. We demonstrate how the key primitives used for sampling-based planning can be efficiently implemented in spaces with finite symmetries. A rigorous theoretical analysis, building upon a study of the geometry of the configuration space, shows improvements in the sample complexity of several standard algorithms. Furthermore, a comprehensive slate of experiments demonstrates the practical improvements in both path length and runtime.
Under review
Multi-Query Shortest-Path Problem in Graphs of Convex Sets
- by Morozov, Savva and Marcucci, Tobia and Amice, Alexandre and Graesdal, Bernhard Paus and Bosworth, Rohan and Parrilo, Pablo A. and Tedrake, Russ
The Shortest-Path Problem in Graph of Convex Sets (SPP in GCS) is a recently developed optimization framework that blends discrete and continuous decision making. Many relevant problems in robotics, such as collision-free motion planning, can be cast and solved as an SPP in GCS, yielding lower-cost solutions and faster runtimes than state-of-the-art algorithms. In this paper, we are motivated by motion planning of robot arms that must operate swiftly in static environments. We consider a multi-query extension of the SPP in GCS, where the goal is to efficiently precompute optimal paths between given sets of initial and target conditions. Our solution consists of two stages. Offline, we use semidefinite programming to compute a coarse lower bound on the problem's cost-to-go function. Then, online, this lower bound is used to incrementally generate feasible paths by solving short-horizon convex programs. For a robot arm with seven joints, our method designs higher quality trajectories up to two orders of magnitude faster than existing motion planners.
To Appear at WAFR 2024
Faster Algorithms for Growing Collision-Free Convex Polytopes in Robot Configuration Space
- by Peter Werner and Thomas Cohn and Rebecca H. Jiang and Tim Seyde and Max Simchowitz and Russ Tedrake and Daniela Rus
We propose two novel algorithms for constructing convex collision-free polytopes in robot configuration space. Finding these polytopes enables the application of stronger motion-planning frameworks such as trajectory optimization with Graphs of Convex Sets and is currently a major roadblock in the adoption of these approaches. In this paper, we build upon IRIS-NP (Iterative Regional Inflation by Semidefinite and Nonlinear Programming) to significantly improve tunability, runtimes, and scaling to complex environments. IRIS-NP uses nonlinear programming paired with uniform random initialization to find configurations on the boundary of the free configuration space. Our key insight is that finding near-by configuration-space obstacles using sampling is inexpensive and greatly accelerates region generation. We propose two algorithms using such samples to either employ nonlinear programming more efficiently (IRIS-NP2 ) or circumvent it altogether using a massively-parallel zero-order optimization strategy (IRIS-ZO). We also propose a termination condition that controls the probability of exceeding a user-specified permissible fraction-in-collision, eliminating a significant source of tuning difficulty in IRIS-NP. We compare performance across eight robot environments, showing that IRIS-ZO achieves an order-of-magnitude speed advantage over IRIS-NP. IRIS-NP2, also significantly faster than IRIS-NP, builds larger polytopes using fewer hyperplanes, enabling faster downstream computation.
To Appear at ISRR 2024
GCS*: Forward Heuristic Search on Implicit Graphs of Convex Sets
- by Shao Yuan Chew Chia and Rebecca H. Jiang and Bernhard Paus Graesdal and Leslie Pack Kaelbling and Russ Tedrake
We consider large-scale, implicit-search-based solutions to Shortest Path Problems on Graphs of Convex Sets (GCS). We propose GCS*, a forward heuristic search algorithm that generalizes A* search to the GCS setting, where a continuous-valued decision is made at each graph vertex, and constraints across graph edges couple these decisions, influencing costs and feasibility. Such mixed discrete-continuous planning is needed in many domains, including motion planning around obstacles and planning through contact. This setting provides a unique challenge for best-first search algorithms: the cost and feasibility of a path depend on continuous-valued points chosen along the entire path. We show that by pruning paths that are cost-dominated over their entire terminal vertex, GCS* can search efficiently while still guaranteeing cost-optimality and completeness. To find satisficing solutions quickly, we also present a complete but suboptimal variation, pruning instead reachability-dominated paths. We implement these checks using polyhedral-containment or sampling-based methods. The former implementation is complete and cost-optimal, while the latter is probabilistically complete and asymptotically cost-optimal and performs effectively even with minimal samples in practice. We demonstrate GCS* on planar pushing tasks where the combinatorial explosion of contact modes renders prior methods intractable and show it performs favorably compared to the state-of-the-art. Project website: https://shaoyuan.cc/research/gcs-star/
Supplemental materials: https://shaoyuan.cc/research/gcs-star/
To Appear at WAFR 2024
Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion
- by Boyuan Chen and Diego Marti Monso and Yilun Du and Max Simchowitz and Russ Tedrake and Vincent Sitzmann
This paper presents Diffusion Forcing, a new training paradigm where a diffusion model is trained to denoise a set of tokens with independent per-token noise levels. We apply Diffusion Forcing to sequence generative modeling by training a causal next-token prediction model to generate one or several future tokens without fully diffusing past ones. Our approach is shown to combine the strengths of next-token prediction models, such as variable-length generation, with the strengths of full-sequence diffusion models, such as the ability to guide sampling to desirable trajectories. Our method offers a range of additional capabilities, such as (1) rolling-out sequences of continuous tokens, such as video, with lengths past the training horizon, where baselines diverge and (2) new sampling and guiding schemes that uniquely profit from Diffusion Forcing's variable-horizon and causal architecture, and which lead to marked performance gains in decision-making and planning tasks. In addition to its empirical success, our method is proven to optimize a variational lower bound on the likelihoods of all subsequences of tokens drawn from the true joint distribution. Project website: https://boyuan.space/diffusion-forcing/
Supplemental materials: https://twitter.com/BoyuanChen0/status/1808538170067407264
To Appear at NeurIPS 2024
Locomotion Group News
-
April 22, 2024. PhD Defense. Congratulations to Lirui Wang for successfully defending his PhD thesis!
April 22, 2024. PhD Defense. Congratulations to Terry Suh for successfully defending his PhD thesis!
April 22, 2024. PhD Defense. Congratulations to Tobia Marcucci for successfully defending his PhD thesis!
September 19, 2023. Video. Check out our work at TRI on Diffusion Policy (towards Large Behavior Models) and the corresponding blog post
June 14, 2023. Award. Congratulations to Terry Suh, Max Simchowitz, and Kaiqing Zhang who's paper titled "Do differentiable simulators give better policy gradients?" was recognized with the "Best Paper (of the year) Award" from the IEEE RAS Technical Committee on Model-based Optimization for Robotics.
January 20, 2023. PhD Defense. Congratulations to Tao Pang for successfully defending his PhD thesis!
July 15, 2022. Award. Congratulations to Terry Suh, Max Simchowitz, and Kaiqing Zhang who's paper titled "Do differentiable simulators give better policy gradients?" was recognized with the "Outstanding Paper Award" at ICML 2022.
January 13, 2022. Award. Congratulations to Alexandre Amice, Hongkai Dai, Pete Werner, and Annan Zhang who's paper titled "Finding and Optimizing Certified, Collision-Free Regions in Configuration Space for Robot Manipulators" was recognized with the "Outstanding Paper Award" at WAFR 2022.
June 17, 2022. PhD Defense. Congratulations to Yunzhu Li for successfully defending his PhD thesis!
June 17, 2022. PhD Defense. Congratulations to Greg Izatt for successfully defending his PhD thesis!
August 15, 2020. Talks on Zoom. For better or worse, most research talks these days 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!
June 27, 2019. Video. Checkout some of the work being done by TRI's manipulation team
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).