3 citations · 6 across the 25 of their papers we have counts for
7 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…
Cost-Driven Representation Learning for Linear Quadratic Gaussian Control: Part II
Yi Tian, Kaiqing Zhang, Russ Tedrake +1
We study the problem of state representation learning for control from partial and potentially high-dimensional observations. We approach this problem via cost-driven state represe…
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…