Papers › Adaptable Human Intention and Trajectory Prediction for Human-Robot Collaboration

Adaptable Human Intention and Trajectory Prediction for Human-Robot Collaboration

11 Sep 2019arXiv:1909.05089links table onlyarchive 2025-07-28

Abulikemu Abuduweili, Siyan Li, Changliu Liu

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To engender safe and efficient human-robot collaboration, it is critical to generate high-fidelity predictions of human behavior. The challenges in making accurate predictions lie in the stochasticity and heterogeneity in human behaviors. This paper introduces a method for human trajectory and intention prediction through a multi-task model that is adaptable across different human subjects. We develop a nonlinear recursive least square parameter adaptation algorithm (NRLS-PAA) to achieve online adaptation. The effectiveness and flexibility of the proposed method has been validated in experiments. In particular, online adaptation can reduce the trajectory prediction error by more than 28% for a new human subject. The proposed human prediction method has high flexibility, data efficiency, and generalizability, which can support fast integration of HRC systems for user-specified tasks.

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