166 citations · 191 across the 9 of their papers we have counts for
9 papers
Splatting Physical Scenes: End-to-End Real-to-Sim from Imperfect Robot Data
Ben Moran, Mauro Comi, Arunkumar Byravan +4
Creating accurate, physical simulations directly from real-world robot motion holds great value for safe, scalable, and affordable robot learning, yet remains exceptionally challen…
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 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…
Replay across Experiments: A Natural Extension of Off-Policy RL
Dhruva Tirumala, Thomas Lampe, Jose Enrique Chen +9
Replaying data is a principal mechanism underlying the stability and data efficiency of off-policy reinforcement learning (RL). We present an effective yet simple framework to exte…
Score-based generative models learn manifold-like structures with constrained mixing
Li Kevin Wenliang, Ben Moran
How do score-based generative models (SBMs) learn the data distribution supported on a low-dimensional manifold? We investigate the score model of a trained SBM through its linear…
Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning
Tuomas Haarnoja, Ben Moran, Guy Lever +25
We investigate whether Deep Reinforcement Learning (Deep RL) is able to synthesize sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be compo…
SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration
Giulia Vezzani, Dhruva Tirumala, Markus Wulfmeier +13
The ability to effectively reuse prior knowledge is a key requirement when building general and flexible Reinforcement Learning (RL) agents. Skill reuse is one of the most common a…
NeRF2Real: Sim2real Transfer of Vision-guided Bipedal Motion Skills using Neural Radiance Fields
Arunkumar Byravan, Jan Humplik, Leonard Hasenclever +8
We present a system for applying sim2real approaches to "in the wild" scenes with realistic visuals, and to policies which rely on active perception using RGB cameras. Given a shor…
Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors
Steven Bohez, Saran Tunyasuvunakool, Philemon Brakel +18
We investigate the use of prior knowledge of human and animal movement to learn reusable locomotion skills for real legged robots. Our approach builds upon previous work on imitati…