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arXiv · 2312.01811

Energy-based Potential Games for Joint Motion Forecasting and Control

Christopher Diehl·Tobias Klosek·Martin Krüger·Nils Murzyn·Timo Osterburg·Torsten Bertram
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Abstract

This work uses game theory as a mathematical framework to address interaction modeling in multi-agent motion forecasting and control. Despite its interpretability, applying game theory to real-world robotics, like automated driving, faces challenges such as unknown game parameters. To tackle these, we establish a connection between differential games, optimal control, and energy-based models, demonstrating how existing approaches can be unified under our proposed Energy-based Potential Game formulation. Building upon this, we introduce a new end-to-end learning application that combines neural networks for game-parameter inference with a differentiable game-theoretic optimization layer, acting as an inductive bias. The analysis provides empirical evidence that the game-theoretic layer adds interpretability and improves the predictive performance of various neural network backbones using two simulations and two real-world driving datasets.

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BibTeXRIS

Christopher Diehl, Tobias Klosek, Martin Krüger, Nils Murzyn, Timo Osterburg, Torsten Bertram. 2023-12-04. Energy-based Potential Games for Joint Motion Forecasting and Control. https://arxiv.org/abs/2312.01811

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