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

no code implementations • 10 Sep 2024 • Maria Bauza, Jose Enrique Chen, Valentin Dalibard, Nimrod Gileadi, Roland Hafner, Murilo F. Martins, Joss Moore, Rugile Pevceviciute, Antoine Laurens, Dushyant Rao, Martina Zambelli, Martin Riedmiller, Jon Scholz, Konstantinos Bousmalis, Francesco Nori, Nicolas Heess

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, from only a sparse reward and a handful of demonstrations in simulation.

Language to Rewards for Robotic Skill Synthesis

no code implementations • 14 Jun 2023 • Wenhao Yu, Nimrod Gileadi, Chuyuan Fu, Sean Kirmani, Kuang-Huei Lee, Montse Gonzalez Arenas, Hao-Tien Lewis Chiang, Tom Erez, Leonard Hasenclever, Jan Humplik, Brian Ichter, Ted Xiao, Peng Xu, Andy Zeng, Tingnan Zhang, Nicolas Heess, Dorsa Sadigh, Jie Tan, Yuval Tassa, Fei Xia

However, since low-level robot actions are hardware-dependent and underrepresented in LLM training corpora, existing efforts in applying LLMs to robotics have largely treated LLMs as semantic planners or relied on human-engineered control primitives to interface with the robot.

In-Context Learning Logical Reasoning

RoboPianist: Dexterous Piano Playing with Deep Reinforcement Learning

1 code implementation • 9 Apr 2023 • Kevin Zakka, Philipp Wu, Laura Smith, Nimrod Gileadi, Taylor Howell, Xue Bin Peng, Sumeet Singh, Yuval Tassa, Pete Florence, Andy Zeng, Pieter Abbeel

Replicating human-like dexterity in robot hands represents one of the largest open problems in robotics.

Benchmarking Multi-Task Learning +2

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

1 code implementation • 12 Oct 2021 • Alex X. Lee, Coline Devin, Yuxiang Zhou, Thomas Lampe, Konstantinos Bousmalis, Jost Tobias Springenberg, Arunkumar Byravan, Abbas Abdolmaleki, Nimrod Gileadi, David Khosid, Claudio Fantacci, Jose Enrique Chen, Akhil Raju, Rae Jeong, Michael Neunert, Antoine Laurens, Stefano Saliceti, Federico Casarini, Martin Riedmiller, Raia Hadsell, Francesco Nori

We study the problem of robotic stacking with objects of complex geometry.

Offline RL Reinforcement Learning (RL) +2