2.1k citations · 2.2k across the 8 of their papers we have counts for
9 papers
Self-Improving Embodied Foundation Models
Seyed Kamyar Seyed Ghasemipour, Ayzaan Wahid, Jonathan Tompson +2
Foundation models trained on web-scale data have revolutionized robotics, but their application to low-level control remains largely limited to behavioral cloning. Drawing inspirat…
Bi-Manual Block Assembly via Sim-to-Real Reinforcement Learning
Satoshi Kataoka, Youngseog Chung, Seyed Kamyar Seyed Ghasemipour +3
Most successes in robotic manipulation have been restricted to single-arm gripper robots, whose low dexterity limits the range of solvable tasks to pick-and-place, inser-tion, and…
Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters
Seyed Kamyar Seyed Ghasemipour, Shixiang Shane Gu, Ofir Nachum
Motivated by the success of ensembles for uncertainty estimation in supervised learning, we take a renewed look at how ensembles of -functions can be leveraged as the primary so…
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
Chitwan Saharia, William Chan, Saurabh Saxena +11
We present Imagen, a text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding. Imagen builds on the power of large tran…
Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning
Seyed Kamyar Seyed Ghasemipour, Daniel Freeman, Byron David +3
Assembly of multi-part physical structures is both a valuable end product for autonomous robotics, as well as a valuable diagnostic task for open-ended training of embodied intelli…
Bi-Manual Manipulation and Attachment via Sim-to-Real Reinforcement Learning
Satoshi Kataoka, Seyed Kamyar Seyed Ghasemipour, Daniel Freeman +1
Most successes in robotic manipulation have been restricted to single-arm robots, which limits the range of solvable tasks to pick-and-place, insertion, and objects rearrangement.…
Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization
Shixiang Shane Gu, Manfred Diaz, Daniel C. Freeman +7
The goal of continuous control is to synthesize desired behaviors. In reinforcement learning (RL)-driven approaches, this is often accomplished through careful task reward engineer…
EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL
Seyed Kamyar Seyed Ghasemipour, Dale Schuurmans, Shixiang Shane Gu
Off-policy reinforcement learning holds the promise of sample-efficient learning of decision-making policies by leveraging past experience. However, in the offline RL setting -- wh…
A Divergence Minimization Perspective on Imitation Learning Methods
Seyed Kamyar Seyed Ghasemipour, Richard Zemel, Shixiang Gu
In many settings, it is desirable to learn decision-making and control policies through learning or bootstrapping from expert demonstrations. The most common approaches under this…