10.1k citations · 17.9k across the 129 of their papers we have counts for
132 papers
Proc4Gem: Foundation models for physical agency through procedural generation
Yixin Lin, Jan Humplik, Sandy H. Huang +18
In robot learning, it is common to either ignore the environment semantics, focusing on tasks like whole-body control which only require reasoning about robot-environment contacts,…
Learning from negative feedback, or positive feedback or both
Abbas Abdolmaleki, Bilal Piot, Bobak Shahriari +9
Existing preference optimization methods often assume scenarios where paired preference feedback (preferred/positive vs. dis-preferred/negative examples) is available. This require…
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…
A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning
Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Avila Pires +7
Learning a good representation is a crucial challenge for Reinforcement Learning (RL) agents. Self-predictive learning provides means to jointly learn a latent representation and d…
Deep Dive into Model-free Reinforcement Learning for Biological and Robotic Systems: Theory and Practice
Yusheng Jiao, Feng Ling, Sina Heydari +3
Animals and robots exist in a physical world and must coordinate their bodies to achieve behavioral objectives. With recent developments in deep reinforcement learning, it is now p…
Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning
Dhruva Tirumala, Markus Wulfmeier, Ben Moran +13
We apply multi-agent deep reinforcement learning (RL) to train end-to-end robot soccer policies with fully onboard computation and sensing via egocentric RGB vision. This setting r…