Ryan Hoque

I am a third year PhD candidate in the UC Berkeley EECS department working on Robotics and Artificial Intelligence. I am advised by Ken Goldberg and am part of the Berkeley Artificial Intelligence Research (BAIR) lab. I have also collaborated with Pieter Abbeel and folks from Google Brain, Meta AI, Honda Research Institute, and Uber ATG (acq. Aurora). My research primarily focuses on algorithms for robot fleet learning from interactive human supervision.

Email  /  CV  /  Google Scholar  /  Github  /  LinkedIn  /  Twitter

profile photo

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:

     Interactive Learning Algorithms

Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human Supervision
Ryan Hoque, Lawrence Yunliang Chen, Satvik Sharma, Karthik Dharmarajan, Brijen Thananjeyan, Pieter Abbeel, Ken Goldberg
Conference on Robot Learning (CoRL) 2022. Oral Presentation (6.5% of papers).
[Paper] [YouTube] [Website] [Twitter TL;DR]

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

ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning
Ryan Hoque, Ashwin Balakrishna, Ellen Novoseller, Daniel S. Brown, Albert Wilcox, Ken Goldberg
Conference on Robot Learning (CoRL) 2021. Oral Presentation (6.5% of papers).
[Paper] [YouTube] [Website] [Twitter TL;DR]

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.

LazyDAgger: Reducing Context Switching in Interactive Imitation Learning
Ryan Hoque, Ashwin Balakrishna, Carl Putterman, Michael Luo, Daniel S. Brown, Daniel Seita, Brijen Thananjeyan, Ellen Novoseller, Ken Goldberg
IEEE Conference on Automation Science and Engineering (CASE) 2021.
[Paper] [Website]

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.

     Learning Algorithms for Deformable Object Manipulation

Learning to Fold Real Garments with One Arm: A Case Study in Cloud-Based Robotics Research
Ryan Hoque*, Kaushik Shivakumar*, Shrey Aeron, Gabriel Deza, Aditya Ganapathi, Adrian Wong, Johnny Lee, Andy Zeng, Vincent Vanhoucke, Ken Goldberg
IEEE International Conference on Robots and Systems (IROS) 2022.
[Paper] [Website]

(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.

VisuoSpatial Foresight for Physical Sequential Fabric Manipulation
Ryan Hoque*, Daniel Seita*, Ashwin Balakrishna, Aditya Ganapathi, Ajay Tanwani, Nawid Jamali, Katsu Yamane, Soshi Iba, Ken Goldberg
Autonomous Robots. Vol 45(5), 2021.
[Paper] [Website]

(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.

VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation
Ryan Hoque*, Daniel Seita*, Ashwin Balakrishna, Aditya Ganapathi, Ajay Tanwani, Nawid Jamali, Katsu Yamane, Soshi Iba, Ken Goldberg
Robotics: Science and Systems (RSS) 2020. (Invited to Autonomous Robots Special Issue)
[Paper] [Website]

(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.

Not Research

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 enjoy an eclectic mix of hobbies including playing the piano and ukulele, reading and writing about philosophy (especially metaphysics), and the outdoors. Some fun facts you may find interesting:

  • 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 working on a significantly improved version, but it's still in progress...
  • In a past life I was a fan of Spider-Man and pranks. I did parkour as an undergrad and I (last I checked) top the Google Images search results for "Bangladesh Spiderman" thanks to the time I went viral on social media (21K+ likes) and international newspapers for clowning around at the 2019 ICC Cricket World Cup in England.
  • The other time I appeared on international newspapers, this time for scaling a tower in Dubrovnik, Croatia during their match in the 2018 FIFA World Cup finals.

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