Building the Next Generation of Intelligent Machines

Progressive Learning for Physics-informed Neural Motion Planning

Abstract— A new semi-linear Eikonal PDE formulation and progressive speed scheduling strategy to solve high-DOF motion planning problems.

RSS'23

NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning

Abstract— Unlike most neural motion planners, NTFields require no expert trajectories for training and instead directly learn to solve Eikonal PDE.

ICLR'23 [SPOTLIGHT]

CoGrasp: 6-DoF Grasp Generation for Human-Robot Collaboration

Abstract— Robot grasp generation contextualizes human social preferences of interacting with daily-life objects for human-robot collaboration.

ICRA'23

Robot Active Neural Sensing and Planning

Abstract— A framework that actively collects the RGBD observations of an unknown confined environment with an in-hand camera and transforms them into interpretable scene representation.

TRO'23

NeRP: Neural Rearrangement Planning for Unknown Objects

Abstract— NeRP is a learning-based approach for multi-step neural object rearrangement planning with never-before-seen objects in the real world.

RSS'21

Constrained Motion Planning Networks X

Abstract— CoMPNetX is a neural planning approach with a fast projection operator for solving constrained manipulation tasks.

TRO'21