3 citations · 6 across the 25 of their papers we have counts for
23 papers · 1 filter
Planning along Differentiable Charts of Constraint Manifolds with General-Purpose IK Solvers
Thomas Cohn, Seiji Shaw, Harel Biggie +3
Planning trajectories for robot manipulators under kinematic equality constraints restricts feasible motions to a measure-zero submanifold of the configuration space, requiring spe…
Revisiting Open-Loop Execution in Robotics: Toward Reactive, Higher-Performing Policies
Michael Zeng, Abhinav Agarwal, Ajay Bati +3
Action chunking --- the practice of predicting a sequence of actions and executing a prefix open-loop --- has emerged as a key enabler of recent progress in imitation learning for…
Training and Evaluating Diffusion Policies with Long Context Lengths
Abhinav Agarwal, Adam Wei, Taylan Kargin +6
Imitation learning has enabled highly-dexterous robotic manipulation from RGB observations. Policies trained with these methods, however, typically condition robot actions on only…
Ambient Diffusion Policy: Imitation Learning from Suboptimal Data in Robotics
Adam Wei, Nicholas Pfaff, Thomas Cohn +4
We propose Ambient Diffusion Policy, a simple and principled method for imitation learning from suboptimal data in robotics. High-quality, task-specific robot data is expensive and…
A Framework for Combining Optimization-Based and Analytic Inverse Kinematics
Thomas Cohn, Lihan Tang, Alexandre Amice +1
Analytic and optimization methods for solving inverse kinematics (IK) problems have been deeply studied throughout the history of robotics. The two strategies have complementary st…
SceneSmith: Agentic Generation of Simulation-Ready Indoor Scenes
Nicholas Pfaff, Thomas Cohn, Sergey Zakharov +2
Simulation has become a key tool for training and evaluating home robots at scale, yet existing environments fail to capture the diversity and physical complexity of real indoor sp…
Large Video Planner Enables Generalizable Robot Control
Boyuan Chen, Tianyuan Zhang, Haoran Geng +9
General-purpose robots require decision-making models that generalize across diverse tasks and environments. Recent works build robot foundation models by extending multimodal larg…
How Well do Diffusion Policies Learn Kinematic Constraint Manifolds?
Lexi Foland, Thomas Cohn, Adam Wei +3
Diffusion policies have shown impressive results in robot imitation learning, even for tasks that require satisfaction of kinematic equality constraints. However, task performance…
Mixed Discrete and Continuous Planning using Shortest Walks in Graphs of Convex Sets
Savva Morozov, Tobia Marcucci, Bernhard Paus Graesdal +3
We study the Shortest-Walk Problem (SWP) in a Graph of Convex Sets (GCS). A GCS is a graph where each vertex is paired with a convex program, and each edge couples adjacent program…
A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation
TRI LBM Team, Jose Barreiros, Andrew Beaulieu +79
Robot manipulation has seen tremendous progress in recent years, with imitation learning policies enabling successful performance of dexterous and hard-to-model tasks. Concurrently…
Steerable Scene Generation with Post Training and Inference-Time Search
Nicholas Pfaff, Hongkai Dai, Sergey Zakharov +2
Training robots in simulation requires diverse 3D scenes that reflect the specific challenges of downstream tasks. However, scenes that satisfy strict task requirements, such as hi…
Dexterous Contact-Rich Manipulation via the Contact Trust Region
H. J. Terry Suh, Tao Pang, Tong Zhao +1
What is a good local description of contact dynamics for contact-rich manipulation, and where can we trust this local description? While many approaches often rely on the Taylor ap…
A New Semidefinite Relaxation for Linear and Piecewise-Affine Optimal Control with Time Scaling
Lujie Yang, Tobia Marcucci, Pablo A. Parrilo +1
We introduce a semidefinite relaxation for optimal control of linear systems with time scaling. These problems are inherently nonconvex, since the system dynamics involves bilinear…
Superfast Configuration-Space Convex Set Computation on GPUs for Online Motion Planning
Peter Werner, Richard Cheng, Tom Stewart +2
In this work, we leverage GPUs to construct probabilistically collision-free convex sets in robot configuration space on the fly. This extends the use of modern motion planning alg…
Scalable Real2Sim: Physics-Aware Asset Generation Via Robotic Pick-and-Place Setups
Nicholas Pfaff, Evelyn Fu, Jeremy Binagia +2
Simulating object dynamics from real-world perception shows great promise for digital twins and robotic manipulation but often demands labor-intensive measurements and expertise. W…
Empirical Analysis of Sim-and-Real Cotraining of Diffusion Policies for Planar Pushing from Pixels
Adam Wei, Abhinav Agarwal, Boyuan Chen +3
Cotraining with demonstration data generated both in simulation and on real hardware has emerged as a promising recipe for scaling imitation learning in robotics. This work seeks t…
Sampling-Based Motion Planning with Discrete Configuration-Space Symmetries
Thomas Cohn, 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 a…
Physics-Driven Data Generation for Contact-Rich Manipulation via Trajectory Optimization
Lujie Yang, H. J. Terry Suh, Tong Zhao +5
We present a low-cost data generation pipeline that integrates physics-based simulation, human demonstrations, and model-based planning to efficiently generate large-scale, high-qu…
Faster Algorithms for Growing Collision-Free Convex Polytopes in Robot Configuration Space
Peter Werner, Thomas Cohn, Rebecca H. Jiang +4
We propose two novel algorithms for constructing convex collision-free polytopes in robot configuration space. Finding these polytopes enables the application of stronger motion-pl…
Planning Shorter Paths in Graphs of Convex Sets by Undistorting Parametrized Configuration Spaces
Shruti Garg, Thomas Cohn, Russ Tedrake
Optimization based motion planning provides a useful modeling framework through various costs and constraints. Using Graph of Convex Sets (GCS) for trajectory optimization gives gu…
Multi-Query Shortest-Path Problem in Graphs of Convex Sets
Savva Morozov, Tobia Marcucci, Alexandre Amice +4
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 pro…
GCS*: Forward Heuristic Search on Implicit Graphs of Convex Sets
Shao Yuan Chew Chia, Rebecca H. Jiang, Bernhard Paus Graesdal +2
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 genera…
PoCo: Policy Composition from and for Heterogeneous Robot Learning
Lirui Wang, Jialiang Zhao, Yilun Du +2
Training general robotic policies from heterogeneous data for different tasks is a significant challenge. Existing robotic datasets vary in different modalities such as color, dept…