# Adaptable Human Intention and Trajectory Prediction for Human-Robot Collaboration

arXiv: https://arxiv.org/abs/1909.05089 · HTML: https://mlsanity.com/paper/1909.05089/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2019.

## Built on by (most cited first, 4 of 4)

- [Adaptive Human Trajectory Prediction via Latent Corridors](https://mlsanity.com/paper/2312.06653/) (2023)
- [Trends in Motion Prediction Toward Deployable and Generalizable Autonomy: A Revisit and Perspectives](https://mlsanity.com/paper/2505.09074/) (2025)
- [Estimating Neural Network Robustness via Lipschitz Constant and Architecture Sensitivity](https://mlsanity.com/paper/2410.23382/) (2024)
- [Data Efficient Human Intention Prediction: Leveraging Neural Network Verification and Expert Guidance](https://mlsanity.com/paper/2108.06871/) (2021)

## Abstract (arXiv)

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.