Dreaming to Assist: Learning to Align with Human Objectives for Shared Control in High-Speed Racing
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912072023932928 |
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| author | DeCastro, Jonathan Silva, Andrew Gopinath, Deepak Sumner, Emily Balch, Thomas M. Dees, Laporsha Rosman, Guy |
| author_facet | DeCastro, Jonathan Silva, Andrew Gopinath, Deepak Sumner, Emily Balch, Thomas M. Dees, Laporsha Rosman, Guy |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_10062 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Dreaming to Assist: Learning to Align with Human Objectives for Shared Control in High-Speed Racing DeCastro, Jonathan Silva, Andrew Gopinath, Deepak Sumner, Emily Balch, Thomas M. Dees, Laporsha Rosman, Guy Robotics Artificial Intelligence Human-Computer Interaction 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. |
| title | Dreaming to Assist: Learning to Align with Human Objectives for Shared Control in High-Speed Racing |
| topic | Robotics Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2410.10062 |