246 citations · 273 across the 5 of their papers we have counts for
8 papers
PLEX: Making the Most of the Available Data for Robotic Manipulation Pretraining
Garrett Thomas, Ching-An Cheng, Ricky Loynd +4
A rich representation is key to general robotic manipulation, but existing approaches to representation learning require large amounts of multimodal demonstrations. In this work we…
Safe Reinforcement Learning by Imagining the Near Future
Garrett Thomas, Yuping Luo, Tengyu Ma
Safe reinforcement learning is a promising path toward applying reinforcement learning algorithms to real-world problems, where suboptimal behaviors may lead to actual negative con…
Model-based Adversarial Meta-Reinforcement Learning
Zichuan Lin, Garrett Thomas, Guangwen Yang +1
Meta-reinforcement learning (meta-RL) aims to learn from multiple training tasks the ability to adapt efficiently to unseen test tasks. Despite the success, existing meta-RL algori…
MOPO: Model-based Offline Policy Optimization
Tianhe Yu, Garrett Thomas, Lantao Yu +5
Offline reinforcement learning (RL) refers to the problem of learning policies entirely from a large batch of previously collected data. This problem setting offers the promise of…
A Model-based Approach for Sample-efficient Multi-task Reinforcement Learning
Nicholas C. Landolfi, Garrett Thomas, Tengyu Ma
The aim of multi-task reinforcement learning is two-fold: (1) efficiently learn by training against multiple tasks and (2) quickly adapt, using limited samples, to a variety of new…
Learning Robotic Assembly from CAD
Garrett Thomas, Melissa Chien, Aviv Tamar +2
In this work, motivated by recent manufacturing trends, we investigate autonomous robotic assembly. Industrial assembly tasks require contact-rich manipulation skills, which are ch…
Learning from the Hindsight Plan -- Episodic MPC Improvement
Aviv Tamar, Garrett Thomas, Tianhao Zhang +2
Model predictive control (MPC) is a popular control method that has proved effective for robotics, among other fields. MPC performs re-planning at every time step. Re-planning is d…
Value Iteration Networks
Aviv Tamar, Yi Wu, Garrett Thomas +2
We introduce the value iteration network (VIN): a fully differentiable neural network with a `planning module' embedded within. VINs can learn to plan, and are suitable for predict…