42 citations · 82 across the 8 of their papers we have counts for
4 papers · 1 filter
On a Connection between Differential Games, Optimal Control, and Energy-based Models for Multi-Agent Interactions
Christopher Diehl, Tobias Klosek, Martin Krüger +2
Game theory offers an interpretable mathematical framework for modeling multi-agent interactions. However, its applicability in real-world robotics applications is hindered by seve…
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…
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…
Online Motion Planning based on Nonlinear Model Predictive Control with Non-Euclidean Rotation Groups
Christoph Rösmann, Artemi Makarow, Torsten Bertram
This paper proposes a novel online motion planning approach to robot navigation based on nonlinear model predictive control. Common approaches rely on pure Euclidean optimization p…