UCSD ADVANCED ROBOTICS AND CONTROLS LAB
Imitation, Reinforcement, and Transfer Learning for Robots in the Wild
Robots in the wild are those that are operating in unseen, complex, challenging environments under new environmental conditions. Most of the time, robot policies and controllers, and even self-sensing (i.e., what is my current status) fail when taken from a controlled environment into the real world.
This research focuses on a fundamentally learning skills either on the fly (online reinforcement), or by leveraging past experiences (imitation and transfer) to as-quickly-as-possible respond to new, unseen environments. This includes robots that create a model of their motions as they move, to ones that solve for controllers or policies online as more data is collected in their new environments. This research also involves transferring knowledge between reality and simulation, where reinforcement learning can be used to guide exploration of the robot's capabilities while exploiting what they have already learned about themselves and their environments.
This research topic is generally open-ended by nature, as robots that monitor their own behavior, that explore new motions, and that adapt to environment variables has broad applications.
Students and Collaborators
Ahmed Qureshi
Jacob Johnson
Jingpei Lu
Zihyun Chiu
Nikhil Shinde
Publications
Bimanual Regrasping for Suture Needles using Reinforcement Learning for Rapid Motion Planning
Z.Y. Chiu, F. Richter, E.K. Funk, R.K. Orosco, M.C. Yip
IEEE Conference on Robotics and Automation (Accepted). Xi'an, China (2021). [arxiv][video]
Robust Keypoint Detection and Pose Estimation of Robot Manipulators with Self-Occlusions via Sim-to-Real Transfer
J. Lu, F. Richter, M. C. Yip
arXiv preprint arXiv:2010.08054 [arxiv] [video]
SOLAR-GP: Sparse, Online, Locally Adaptive Regression using Gaussian Processes for Bayesian Robot Model Learning and Control
B. P. Wilcox, M.C. Yip
IEEE Robotics and Automation Letters. vol. 5, no.2, pp. 2832-2839, 2020. [website]
Active Continual Learning for Planning and Navigation
A.H. Qureshi, Y.L. Miao, M.C. Yip
ICML 2020 Workshop on Real World Experiment Design and Active Learning. [pdf]
Composing Task-Agnostic Policies with Deep Reinforcement Learning
A.H.Qureshi, J.J. Johnson, Y. Qin, T. Henderson, B. Boots, M.C. Yip
International Conference on Learning Representations- ICLR2020. April 26-30, 2020. Addis Ababa, Ethiopia. (Accepted). [arxiv][website]
Adversarial Imitation via Variational Inverse Reinforcement Learning
A.H.Qureshi, B. Boots, M.C. Yip
Int. Conference on Learning Representations- ICLR2019. May 6-9, 2019. New Orleans, LA. [pdf,website,vid]
Open-Sourced Reinforcement Learning Environments for Surgical Robotics
F. Richter, R. K. Orosco, M.C. Yip