284 citations · 822 across the 48 of their papers we have counts for
6 papers · 1 filter
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…
Data-efficient Hindsight Off-policy Option Learning
Markus Wulfmeier, Dushyant Rao, Roland Hafner +8
We introduce Hindsight Off-policy Options (HO2), a data-efficient option learning algorithm. Given any trajectory, HO2 infers likely option choices and backpropagates through the d…
Simple Sensor Intentions for Exploration
Tim Hertweck, Martin Riedmiller, Michael Bloesch +5
Modern reinforcement learning algorithms can learn solutions to increasingly difficult control problems while at the same time reduce the amount of prior knowledge needed for their…
Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics
Michael Neunert, Abbas Abdolmaleki, Markus Wulfmeier +7
Many real-world control problems involve both discrete decision variables - such as the choice of control modes, gear switching or digital outputs - as well as continuous decision…