103 citations · 129 across the 13 of their papers we have counts for
6 papers · 1 filter
RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
Charles Xu, Qiyang Li, Jianlan Luo +1
Recent advances in robotic foundation models have enabled the development of generalist policies that can adapt to diverse tasks. While these models show impressive flexibility, th…
Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning
Jianlan Luo, Charles Xu, Jeffrey Wu +1
Reinforcement learning (RL) holds great promise for enabling autonomous acquisition of complex robotic manipulation skills, but realizing this potential in real-world settings has…
Octo: An Open-Source Generalist Robot Policy
Octo Model Team, Dibya Ghosh, Homer Walke +16
Large policies pretrained on diverse robot datasets have the potential to transform robotic learning: instead of training new policies from scratch, such generalist robot policies…
Yell At Your Robot: Improving On-the-Fly from Language Corrections
Lucy Xiaoyang Shi, Zheyuan Hu, Tony Z. Zhao +5
Hierarchical policies that combine language and low-level control have been shown to perform impressively long-horizon robotic tasks, by leveraging either zero-shot high-level plan…
SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning
Jianlan Luo, Zheyuan Hu, Charles Xu +7
In recent years, significant progress has been made in the field of robotic reinforcement learning (RL), enabling methods that handle complex image observations, train in the real…
FMB: a Functional Manipulation Benchmark for Generalizable Robotic Learning
Jianlan Luo, Charles Xu, Fangchen Liu +5
In this paper, we propose a real-world benchmark for studying robotic learning in the context of functional manipulation: a robot needs to accomplish complex long-horizon behaviors…