2 citations · 3 across the 2 of their papers we have counts for
2 papers
cs.RO2022★ 2 cited
Differentiable Constrained Imitation Learning for Robot Motion Planning and Control
Christopher Diehl, Janis Adamek, Martin Krüger +2
Motion planning and control are crucial components of robotics applications like automated driving. Here, spatio-temporal hard constraints like system dynamics and safety boundarie…
cs.RO2021★ 1 cited
UMBRELLA: Uncertainty-Aware Model-Based Offline Reinforcement Learning Leveraging Planning
Christopher Diehl, Timo Sievernich, Martin Krüger +2
Offline reinforcement learning (RL) provides a framework for learning decision-making from offline data and therefore constitutes a promising approach for real-world applications a…