activity
20212025
most citedLanguage to Rewards for Robotic Skill Synthesis

39 citations · 73 across the 7 of their papers we have counts for

collaborators

7 papers

cs.RO2025

ExoStart: Efficient learning for dexterous manipulation with sensorized exoskeleton demonstrations

Zilin Si, Jose Enrique Chen, M. Emre Karagozler +15

Recent advancements in teleoperation systems have enabled high-quality data collection for robotic manipulators, showing impressive results in learning manipulation at scale. This…

cs.RO2024

DemoStart: Demonstration-led auto-curriculum applied to sim-to-real with multi-fingered robots

Maria Bauza, Jose Enrique Chen, Valentin Dalibard +13

We present DemoStart, a novel auto-curriculum reinforcement learning method capable of learning complex manipulation behaviors on an arm equipped with a three-fingered robotic hand…

cs.RO2024★ 1 cited

Learning to Learn Faster from Human Feedback with Language Model Predictive Control

Jacky Liang, Fei Xia, Wenhao Yu +47

Large language models (LLMs) have been shown to exhibit a wide range of capabilities, such as writing robot code from language commands -- enabling non-experts to direct robot beha…

cs.RO2023★ 39 cited

Language to Rewards for Robotic Skill Synthesis

Wenhao Yu, Nimrod Gileadi, Chuyuan Fu +17

Large language models (LLMs) have demonstrated exciting progress in acquiring diverse new capabilities through in-context learning, ranging from logical reasoning to code-writing.…

cs.RO2023★ 5 cited

RoboPianist: Dexterous Piano Playing with Deep Reinforcement Learning

Kevin Zakka, Philipp Wu, Laura Smith +8

Replicating human-like dexterity in robot hands represents one of the largest open problems in robotics. Reinforcement learning is a promising approach that has achieved impressive…

cs.RO2022★ 12 cited

Predictive Sampling: Real-time Behaviour Synthesis with MuJoCo

Taylor Howell, Nimrod Gileadi, Saran Tunyasuvunakool +3

We introduce MuJoCo MPC (MJPC), an open-source, interactive application and software framework for real-time predictive control, based on MuJoCo physics. MJPC allows the user to ea…

cs.RO2021★ 16 cited

Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes

Alex X. Lee, Coline Devin, Yuxiang Zhou +18

We study the problem of robotic stacking with objects of complex geometry. We propose a challenging and diverse set of such objects that was carefully designed to require strategie…