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arXiv · 2410.10062

Dreaming to Assist: Learning to Align with Human Objectives for Shared Control in High-Speed Racing

Jonathan DeCastro·Andrew Silva·Deepak Gopinath·Emily Sumner·Thomas M. Balch·Laporsha Dees·Guy Rosman
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Abstract

Tight coordination is required for effective human-robot teams in domains involving fast dynamics and tactical decisions, such as multi-car racing. In such settings, robot teammates must react to cues of a human teammate's tactical objective to assist in a way that is consistent with the objective (e.g., navigating left or right around an obstacle). To address this challenge, we present Dream2Assist, a framework that combines a rich world model able to infer human objectives and value functions, and an assistive agent that provides appropriate expert assistance to a given human teammate. Our approach builds on a recurrent state space model to explicitly infer human intents, enabling the assistive agent to select actions that align with the human and enabling a fluid teaming interaction. We demonstrate our approach in a high-speed racing domain with a population of synthetic human drivers pursuing mutually exclusive objectives, such as "stay-behind" and "overtake". We show that the combined human-robot team, when blending its actions with those of the human, outperforms the synthetic humans alone as well as several baseline assistance strategies, and that intent-conditioning enables adherence to human preferences during task execution, leading to improved performance while satisfying the human's objective.

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Jonathan DeCastro, Andrew Silva, Deepak Gopinath, Emily Sumner, Thomas M. Balch, Laporsha Dees, Guy Rosman. 2024-10-14. Dreaming to Assist: Learning to Align with Human Objectives for Shared Control in High-Speed Racing. https://arxiv.org/abs/2410.10062

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