3 papers
cs.RO2022
End-to-End Learning of Hybrid Inverse Dynamics Models for Precise and Compliant Impedance Control
Moritz Reuss, Niels van Duijkeren, Robert Krug +3
It is well-known that inverse dynamics models can improve tracking performance in robot control. These models need to precisely capture the robot dynamics, which consist of well-un…
cs.RO2021
Learning Forceful Manipulation Skills from Multi-modal Human Demonstrations
An T. Le, Meng Guo, Niels van Duijkeren +4
Learning from Demonstration (LfD) provides an intuitive and fast approach to program robotic manipulators. Task parameterized representations allow easy adaptation to new scenes an…
cs.RO2018
Safe-To-Explore State Spaces: Ensuring Safe Exploration in Policy Search with Hierarchical Task Optimization
Jens Lundell, Robert Krug, Erik Schaffernicht +2
Policy search reinforcement learning allows robots to acquire skills by themselves. However, the learning procedure is inherently unsafe as the robot has no a-priori way to predict…