Ryan Hoque

I am a third year PhD student in the UC Berkeley EECS department studying Robotics and Artificial Intelligence. I am advised by Prof. Ken Goldberg and am part of the Berkeley Artificial Intelligence Research (BAIR) lab. My work focuses on algorithms for interactive learning and execution, where control is shared intelligently between learning robots and their human supervisors.

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My PhD research is focused on the development of interactive imitation and reinforcement learning algorithms with application to complex robotic tasks. In my undergraduate and Master's research, I worked on algorithms for robotic manipulation of deformable objects. In addition to selected publications below (see my CV for the full list), check out:

• My talk at CoRL 2021 in London on our recent work in interactive learning.
• My M.S. thesis: "Robotic Fabric Manipulation with Deep Imitation Learning and Reinforcement Learning in Simulation."
• Issue #238 of the Import AI newsletter, read by more than 10,000 AI experts, featuring our work at the top.

We introduce new formalism, algorithms, and open-source benchmarks for "Interactive Fleet Learning": interactive learning with multiple robots and multiple humans.

A novel interactive imitation learning algorithm that reasons about both state novelty and risk to actively query for human interventions more efficiently than prior algorithms.

We propose "context switching" between human and robot control as a measure of supervisor burden in interactive imitation learning, and propose an algorithm that reduces context switching during training and execution.

(In collaboration with Robotics at Google) We perform the first systematic benchmarking of fabric manipulation algorithms with Google Reach, a prototype hardware testbed for low-latency remote robot control over the Internet.

(In collaboration with Honda Research Institute) Journal paper extending the VSF conference paper below with new datasets, visual dynamics models, cost functions, optimizers, and physical experiments.

(In collaboration with Honda Research Institute) A novel model-based reinforcement learning technique that trains a visual dynamics model for sequentially manipulating fabric toward a variety of goal images, entirely from random interaction RGBD data in simulation.

I'm a Bay Area native and a lifelong bear: I earned my B.S. and M.S. at UC Berkeley and am still here for my PhD. Outside of research, I enjoy an eclectic mix of hobbies including freestyle rap, playing the piano and ukulele, reading and writing about philosophy, chess, and the outdoors. Various fun facts you may find interesting:

Website template from BAIR alum Jon Barron. This template seems to be quite popular!