Ryan Hoque

I am a final year PhD candidate in the UC Berkeley EECS department and a researcher in Robotics and Artificial Intelligence. I am advised by Ken Goldberg and am part of the Berkeley Artificial Intelligence Research (BAIR) lab. I have also been fortunate to collaborate with researchers from Google DeepMind, NVIDIA SRL, Meta FAIR, Honda Research Institute, Siemens AI, and Uber ATG (acq. Aurora). My research primarily consists of scalable algorithms for robot imitation learning.

Email  /  CV  /  Google Scholar  /  LinkedIn  /  Twitter

My PhD research is focused on the development of interactive imitation and reinforcement learning algorithms that scale to large robot fleets performing complex tasks (e.g., manipulation). In my undergraduate and Master's research, I worked on learning 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 2022 in Auckland, New Zealand and BAIR Blog post on Interactive Fleet Learning. This is ideal if you're looking for a quick and accessible highlight of my recent work.
• Issue #238 of the Import AI newsletter featuring my Master's research.
• University courses that have featured my research, including CS 16-881: Deep Reinforcement Learning for Robotics at Carnegie Mellon University, CS 6960: Human-AI Alignment at the University of Utah, and CS 343: Artificial Intelligence at the University of Texas, Austin.

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

(In collaboration with Google DeepMind and 33 academic labs) Cross-embodiment fleet learning with heterogeneous robots. An open-source dataset of 1M+ robot trajectories from 22 robot embodiments, and results with robot foundation models trained on this data.

An extension of Interactive Fleet Learning to heterogeneous and multimodal human supervision, including a novel approach for quantifying uncertainty in energy-based models.

(In collaboration with Google DeepMind) 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) 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. in EECS at UC Berkeley and am still here for my PhD. Outside of research, I have an eclectic mix of hobbies including playing piano covers of heavy metal bands, reading and writing about philosophy (especially metaphysics), traveling, and enjoying the outdoors. For the philosophically inclined, I wrote an essay in 2020 in which I synthesize Western and Eastern thought to answer the age-old "meaning of life" question. I'm currently working on a significantly revised version as a full-length book, but it's very much still in progress...!

Website template from BAIR alum Jon Barron.