21 citations · 38 across the 6 of their papers we have counts for
7 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…
Implicit Offline Reinforcement Learning via Supervised Learning
Alexandre Piche, Rafael Pardinas, David Vazquez +2
Offline Reinforcement Learning (RL) via Supervised Learning is a simple and effective way to learn robotic skills from a dataset collected by policies of different expertise levels…
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.…
Concept Learning with Energy-Based Models
Igor Mordatch
Many hallmarks of human intelligence, such as generalizing from limited experience, abstract reasoning and planning, analogical reasoning, creative problem solving, and capacity fo…