5 papers
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…
Toward Learning POMDPs Beyond Full-Rank Actions and State Observability
Seiji Shaw, Travis Manderson, Chad Kessens +1
We are interested in enabling autonomous agents to learn and reason about systems with hidden states, such as locking mechanisms. We cast this problem as learning the parameters of…
Learning Attentive Neural Processes for Planning with Pushing Actions
Atharv Jain, Seiji Shaw, Nicholas Roy
Our goal is to enable robots to plan sequences of tabletop actions to push a block with unknown physical properties to a desired goal pose. We approach this problem by learning the…
Towards Practical Finite Sample Bounds for Motion Planning in TAMP
Seiji Shaw, Aidan Curtis, Leslie Pack Kaelbling +2
When using sampling-based motion planners, such as PRMs, in configuration spaces, it is difficult to determine how many samples are required for the PRM to find a solution consiste…
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…