Ryan Hoque

I am a first year PhD student in the UC Berkeley EECS department studying Robotics and Artificial Intelligence. I am advised by Prof. Ken Goldberg and am associated with Berkeley Artificial Intelligence Research (BAIR).

Before starting my PhD program, I spent some time in industry working on autonomous driving at Uber's Advanced Technologies Group (ATG) and earned my B.S. and M.S. summa cum laude in Electrical Engineering and Computer Science, also from UC Berkeley (Go Bears!). Outside of research, I enjoy an eclectic mix of hobbies including rapping (both written and freestyle), playing the piano, reading and writing about philosophy, chess, and parkour.

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My master's and undergraduate research was focused on robotic manipulation of deformable objects. We have explored several deep reinforcement learning and imitation learning techniques in simulation ranging from visual foresight to dense object nets to achieve state-of-the-art results on fabric manipulation with real robotic systems. See our BAIR Blog post (in collaboration with Pieter Abbeel's lab) for a summary of our results and my M.S. thesis for a more thorough treatment.

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.
Project Website

By applying a model-based reinforcement learning technique we call VisuoSpatial Foresight, we train a visual dynamics model and use it to sequentially manipulate fabric toward a variety of goal images, entirely from random interaction RGBD data in simulation.

Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor
Daniel Seita, Aditya Ganapathi, Ryan Hoque, Minho Hwang, Edward Cen, Ajay Tanwani, Ashwin Balakrishna, Brijen Thananjeyan, Jeffrey Ichnowski, Nawid Jamali, Katsu Yamane, Soshi Iba, John Canny, Ken Goldberg
To appear at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020.
Project Website

In this paper we learn to smooth real fabric from arbitrarily complex starting states by leveraging our access to ground truth state in simulation.

Learning to Smooth and Fold Real Fabric Using Dense Object Descriptors Trained on Synthetic Color Images
Aditya Ganapathi, Priya Sundaresan, Brijen Thananjeyan, Ashwin Balakrishna, Daniel Seita, Jennifer Grannen, Minho Hwang, Ryan Hoque, Joseph Gonzalez, Nawid Jamali, Katsu Yamane, Soshi Iba, Ken Goldberg
arXiv preprint.
Project Website

Here we learn correspondences between images of fabric to capture visual structure (as opposed to dynamical structure) and specify policies by demonstrating actions on a standardized configuration of the fabric (e.g. smooth).


Various other things you may find interesting.

  • An essay in which I provide a comprehensive answer to "what's the meaning of life?" that isn't nihilism, existentialism, or organized religion. If nothing else, please check this one out; I've concentrated years of soul-searching insights into this one piece.
  • A writeup on the current state of the Quantum Approximate Optimization Algorithm.
  • The time I went viral on social media and international newspapers for clowning around at the ICC Cricket World Cup 2019 in England.
  • The other time I appeared on international newspapers, this time for scaling a tower in Dubrovnik, Croatia during the FIFA World Cup 2018 finals.
  • A picture I drew of myself as an anime character. I'm not entirely sure what compelled me to do this.

Website template from Jon Barron.