22 citations · 47 across the 12 of their papers we have counts for
4 papers · 1 filter
Mastering Stacking of Diverse Shapes with Large-Scale Iterative Reinforcement Learning on Real Robots
Thomas Lampe, Abbas Abdolmaleki, Sarah Bechtle +12
Reinforcement learning solely from an agent's self-generated data is often believed to be infeasible for learning on real robots, due to the amount of data needed. However, if done…
Foundations for Transfer in Reinforcement Learning: A Taxonomy of Knowledge Modalities
Markus Wulfmeier, Arunkumar Byravan, Sarah Bechtle +2
Contemporary artificial intelligence systems exhibit rapidly growing abilities accompanied by the growth of required resources, expansive datasets and corresponding investments int…
Equivariant Data Augmentation for Generalization in Offline Reinforcement Learning
Cristina Pinneri, Sarah Bechtle, Markus Wulfmeier +4
We present a novel approach to address the challenge of generalization in offline reinforcement learning (RL), where the agent learns from a fixed dataset without any additional in…
A Generalist Dynamics Model for Control
Ingmar Schubert, Jingwei Zhang, Jake Bruce +7
We investigate the use of transformer sequence models as dynamics models (TDMs) for control. We find that TDMs exhibit strong generalization capabilities to unseen environments, bo…