Brian Yang

I am a PhD student in the Robotics Institute at Carnegie Mellon's School of Computer Science. I completed my undergrad at UC Berkeley majoring in CS, where I did research involving robot learning with Professor Sergey Levine in the Berkeley AI Research Lab. Previously, I also worked with Professor Kris Pister on microrobot locomotion. I also worked at Facebook AI Research with Roberto Calandra on dextrous manipulation.

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Research
mavric MAVRIC: Morphology-Agnostic Visual Robotic Control
Brian Yang*, Dinesh Jayaraman*, Glen Berseth, Alexei Efros, Sergey Levine
RA-L + ICRA 2020
preprint / project page

We propose MAVRIC, an approach for visumotor control that requires minimal prior knowledge of the robot's morphology and works using less than 20 seconds of training data. We demonstrate results in a variety of settings, including shaky handheld cameras, amputated arms, and previously unseen tools.

mavric DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation
Mike Lambeta*, Po-Wei Chou*, Stephen Tian*, Brian Yang*, Benjamin Maloon, Victoria Rose Most, Dave Stroud, Raymond Santos, Ahmad Byagowi, Gregg Kammerer, Dinesh Jayaraman, Roberto Calandra
RA-L + ICRA 2020
arXiv / project page

We introduce DIGIT, an inexpensive, compact, and high-resolution tactile sensor for in-hand manipulation. We demonstrate its capabilities by training deep neural network model-based controllers to manipulate marbles with an Allegro hand.

replab REPLAB: A Reproducible Low-Cost Arm Benchmark Platform for Robotic Learning
Brian Yang, Dinesh Jayaraman, Sergey Levine
ICRA 2019
arxiv (extended technical report) / project page / code

We present a reproducible, low-cost, self-contained hardware stack for benchmarking vision-based manipulation tasks.

morphology Data-efficient Learning of Morphology and Controller for a Microrobot
Thomas Liao, Grant Wang, Brian Yang, Rene Lee, Kris Pister, Sergey Levine, Roberto Calandra
ICRA 2019
arxiv / project page / code

Robot design is often a slow and difficult process requiring the iterative construction and testing of prototypes. We propose HPC-BBO, a novel algorithm to efficiently and automatically design hardware configurations and controllers for locomotion.

prototype Learning Flexible and Reusable Locomotion Primitives for a Microrobot
Brian Yang, Grant Wang, Roberto Calandra, Daniel Contreras, Sergey Levine, Kris Pister
RA-L + ICRA 2018
arxiv / project page / code

We validate Bayesian optimization as a data-efficient method to design gaits for legged microrobots. We also propose a novel approach to learning motor primitives by converting a contextual policy search problem into a multi-objective optimization task.


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