# Meta-Learning-Based Mode Recognition and Adaptive Control for Human-Robot Interaction

- Source: arXiv cs.RO
- Published: 2026-10-22T04:00:00.000Z
- RoboHot: https://robohot.news/en/items/oq77ju3lm56vp63yuore15o3z
- Original: https://arxiv.org/abs/2610.28761

## Summary

The framework uses a meta-learning-trained neural network to recognize two human-robot interaction modes, active guidance and passive following, without manually setting switching thresholds. The passive mode recognition rate rose from 90.1% to 99.1%, and the active mode from 79.8% to 97.1%. An adaptive meta-learning controller can compensate for human disturbances and adapt online to varying interaction forces. A multi-trajectory meta-learning algorithm jointly learns an environment basis representation and force-related parameters to handle unmodeled dynamics. Experiments verified its robust control and reliable mode recognition performance.