10 citations · 11 across the 3 of their papers we have counts for
7 papers
Differentiable Simulation of Hard Contacts with Soft Gradients for Learning and Control
Anselm Paulus, A. René Geist, Pierre Schumacher +3
Contact forces introduce discontinuities into robot dynamics that severely limit the use of simulators for gradient-based optimization. Penalty-based simulators such as MuJoCo, sof…
Learning to Control Emulated Muscles in Real Robots: Towards Exploiting Bio-Inspired Actuator Morphology
Pierre Schumacher, Lorenz Krause, Jan Schneider +3
Recent studies have demonstrated the immense potential of exploiting muscle actuator morphology for natural and robust movement -- in simulation. A validation on real robotic hardw…
Generating Realistic Arm Movements in Reinforcement Learning: A Quantitative Comparison of Reward Terms and Task Requirements
Jhon P. F. Charaja, Isabell Wochner, Pierre Schumacher +7
The mimicking of human-like arm movement characteristics involves the consideration of three factors during control policy synthesis: (a) chosen task requirements, (b) inclusion of…
Identifying Policy Gradient Subspaces
Jan Schneider, Pierre Schumacher, Simon Guist +4
Policy gradient methods hold great potential for solving complex continuous control tasks. Still, their training efficiency can be improved by exploiting structure within the optim…
MIMo: A Multi-Modal Infant Model for Studying Cognitive Development
Dominik Mattern, Pierre Schumacher, Francisco M. López +4
Human intelligence and human consciousness emerge gradually during the process of cognitive development. Understanding this development is an essential aspect of understanding the…
Investigating the Impact of Action Representations in Policy Gradient Algorithms
Jan Schneider, Pierre Schumacher, Daniel Häufle +2
Reinforcement learning~(RL) is a versatile framework for learning to solve complex real-world tasks. However, influences on the learning performance of RL algorithms are often poor…