13 papers
ReVAMP: Vector-Accelerated Motion Planning for Kinematically-Constrained Systems via Reparameterization
Shrutheesh R. Iyer, Thomas Cohn, Zachary Kingston
Robots often must satisfy one or more constraints during motion planning for real-world tasks. When such constraints reduce the valid configuration space to a measure-zero subset,…
Induced Riemannian Metrics for Motion Planning with Constraints
Phone Thiha Kyaw, Thomas Cohn, Miguel Angel Rogel Garcia +1
In constrained motion planning problems, task and loop-closure constraints restrict a robot's motion to a curved, lower-dimensional submanifold of its configuration space. Planners…
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…
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…
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…
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…
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…
Constrained Bimanual Planning with Analytic Inverse Kinematics
Thomas Cohn, Seiji Shaw, Max Simchowitz +1
In order for a bimanual robot to manipulate an object that is held by both hands, it must construct motion plans such that the transformation between its end effectors remains fixe…
Non-Euclidean Motion Planning with Graphs of Geodesically-Convex Sets
Thomas Cohn, Mark Petersen, Max Simchowitz +1
Computing optimal, collision-free trajectories for high-dimensional systems is a challenging problem. Sampling-based planners struggle with the dimensionality, whereas trajectory o…
Topologically-Informed Atlas Learning
Thomas Cohn, Nikhil Devraj, Odest Chadwicke Jenkins
We present a new technique that enables manifold learning to accurately embed data manifolds that contain holes, without discarding any topological information. Manifold learning a…