Our research vision is to enable robotic systems that: learn hierarchical control tasks by watching humans, seamlessly interact and collaborate with humans, and learn to improve performance and acquire new skills through self-practice. And our approach to these challenges develops algorithmic methods to enable efficient robot learning for long-term sequential tasks through Generalizable Autonomy.
Our current research focuses on machine learning algorithms for perception and control in robotics. The principal focus of this research is to understand representations and algorithms to enable the efficiency and generality of learning for interaction in autonomous agents. We work on challenging open problems at the intersection of computer vision, machine learning, and robotics. We develop algorithms and systems that unify reinforcement learning, control theoretic modeling, and 2D/3D visual scene understanding to teach robots to perceive and to interact with the physical world.
Research Interests: Robotics, Reinforcement Learning & Optimal Control, Computer Vision
Current Applications: Mobile-Manipulation in Retail/Warehouse, personal/home, and surgical/medical robotics.
We are accepting new students at all levels!
Please see openings for details.
|Mar 5, 2020||Organizing RSS workshop on Action Representation Learning|
|Mar 5, 2020||Organizing RSS workshop on Visual Learning and Reasoning for Robotics|
|Jan 31, 2020||Four new papers accepted at ICRA and RA-L!|
|Jan 15, 2020||Organizing ICLR workshop on Neural ODEs in Physical Sciences|
|Dec 10, 2019||Awarded with CIFAR AI Chair 2019|