Building the Next Generation of Intelligent Machines

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

Model-free Neural Lyapunov Control for Safe Robot Navigation

Abstract— Co-learning a Twin Neural Lyapunov Function and Deep Reinforcement Learning control policy for locomotion tasks.

IROS'22

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