Electronic Resource
Learning to Play Trajectory Games Against Opponents with Unknown Objectives
| العنوان: | Learning to Play Trajectory Games Against Opponents with Unknown Objectives |
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| المؤلفون: | Liu, Xinjie (author), Peters, L. (author), Alonso-Mora, J. (author) |
| بيانات النشر: | 2023 |
| نوع الوثيقة: | Electronic Resource |
| مستخلص: | Many autonomous agents, such as intelligent vehicles, are inherently required to interact with one another. Game theory provides a natural mathematical tool for robot motion planning in such interactive settings. However, tractable algorithms for such problems usually rely on a strong assumption, namely that the objectives of all players in the scene are known. To make such tools applicable for ego-centric planning with only local information, we propose an adaptive model-predictive game solver, which jointly infers other players' objectives online and computes a corresponding generalized Nash equilibrium (GNE) strategy. The adaptivity of our approach is enabled by a differentiable trajectory game solver whose gradient signal is used for maximum likelihood estimation (MLE) of opponents' objectives. This differentiability of our pipeline facilitates direct integration with other differentiable elements, such as neural networks (NNs). Furthermore, in contrast to existing solvers for cost inference in games, our method handles not only partial state observations but also general inequality constraints. In two simulated traffic scenarios, we find superior performance of our approach over both existing game-theoretic methods and non-game-theoretic model-predictive control (MPC) approaches. We also demonstrate our approach's real-time planning capabilities and robustness in two-player hardware experiments. Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Learning & Autonomous Control |
| مصطلحات الفهرس: | Collision avoidance, Games, human-aware motion planning, integrated planning and learning, Maximum likelihood estimation, multi-robot systems, Optimization, Planning, Robots, Trajectory, Trajectory games, journal article |
| DOI: | 10.1109.LRA.2023.3280809 |
| URL: | IEEE Robotics and Automation Letters--2377-3766--c2bc75c0-936b-445a-aeb4-917da6664820 |
| الاتاحة: | Open access content. Open access content © 2023 Xinjie Liu, L. Peters, J. Alonso-Mora |
| ملاحظة: | English |
| Other Numbers: | NLTUD oai:tudelft.nl:uuid:fafde1ec-a419-4ad7-817e-d8a4e384ca6c doi:10.1109/LRA.2023.3280809 1416846873 |
| المصدر المساهم: | DELFT UNIV OF TECHNOL From OAIster®, provided by the OCLC Cooperative. |
| رقم الانضمام: | edsoai.on1416846873 |
| قاعدة البيانات: | OAIster |
| DOI: | 10.1109.LRA.2023.3280809 |
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