2023-12-02-diehl23a.md

title	section	openreview	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Energy-based Potential Games for Joint Motion Forecasting and Control	Poster	Eyb4e3GBuuR	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.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	diehl23a	0	Energy-based Potential Games for Joint Motion Forecasting and Control	3112	3141	3112-3141	3112	false	Diehl, Christopher and Klosek, Tobias and Krueger, Martin and Murzyn, Nils and Osterburg, Timo and Bertram, Torsten	given family Christopher Diehl given family Tobias Klosek given family Martin Krueger given family Nils Murzyn given family Timo Osterburg given family Torsten Bertram	2023-12-02		Proceedings of The 7th Conference on Robot Learning	229	inproceedings	date-parts 2023 12 2	https://proceedings.mlr.press/v229/diehl23a/diehl23a.pdf