27 citations · 84 across the 10 of their papers we have counts for
13 papers
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…
Is Curiosity All You Need? On the Utility of Emergent Behaviours from Curious Exploration
Oliver Groth, Markus Wulfmeier, Giulia Vezzani +5
Curiosity-based reward schemes can present powerful exploration mechanisms which facilitate the discovery of solutions for complex, sparse or long-horizon tasks. However, as the ag…
Collect & Infer -- a fresh look at data-efficient Reinforcement Learning
Martin Riedmiller, Jost Tobias Springenberg, Roland Hafner +1
This position paper proposes a fresh look at Reinforcement Learning (RL) from the perspective of data-efficiency. Data-efficient RL has gone through three major stages: pure on-lin…
Representation Matters: Improving Perception and Exploration for Robotics
Markus Wulfmeier, Arunkumar Byravan, Tim Hertweck +8
Projecting high-dimensional environment observations into lower-dimensional structured representations can considerably improve data-efficiency for reinforcement learning in domain…
"What, not how": Solving an under-actuated insertion task from scratch
Giulia Vezzani, Michael Neunert, Markus Wulfmeier +7
Robot manipulation requires a complex set of skills that need to be carefully combined and coordinated to solve a task. Yet, most ReinforcementLearning (RL) approaches in robotics…
Towards General and Autonomous Learning of Core Skills: A Case Study in Locomotion
Roland Hafner, Tim Hertweck, Philipp Klöppner +6
Modern Reinforcement Learning (RL) algorithms promise to solve difficult motor control problems directly from raw sensory inputs. Their attraction is due in part to the fact that t…