Building the Next Generation of Intelligent Machines


What we do

We perform fundamental and applied research in robotics, machine learning, and artificial intelligence to design and develop intelligent systems. Our work touches on various problems, including dextrous manipulation and control, mobile navigation, human-robot collaboration, autonomous driving, and healthcare. Read more

Human-aware Grasping

Human-aware Grasping

Active Sensing

Constrained Manipulation

Visual Navigation

Neural Motion Planning

Neural Rearrangment Planning

Deep Reinforcement Learning

Latest News


Paper on Neural Lyapunov-based Safe Control accepted at IROS'22!


CoRAL Lab welcomes new Ph.D. students Ruiqi Ni, Hanwen Ren, and Zixing Wang!


Paper on Constrained Manipulation Planning accepted at TRO'21!


Paper on Neural Rearragment Planning accepted at RSS'21!

Recent Publications

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.

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.

Constrained Motion Planning Networks X

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

Composing Task-Agnostic Policies via Deep Reinforcement Learning

Abstract— A deep reinforcement learning-based skill transfer and composition method for robot location tasks.

Adversarial Imitation Via Variational Inverse Reinforcement Learning

Abstract— Empowerment-regularized Adversarial Inverse Reinforcement Learning to infer transferable near-optimal rewards functions.

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Research Videos

Neural Rearragment Planning - RSS 2021
Motion Planning Networks
Dynamically Constrained Motion

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