Text2Interaction: Establishing Safe and Preferable Human-Robot Interaction
<|reference_start|>Text2Interaction: Establishing Safe and Preferable Human-Robot Interaction: Adjusting robot behavior to human preferences can require intensive human feedback, preventing quick adaptation to new users and changing circumstances. Moreover, current approaches typically treat user preferences as a reward, which requires a manual balance between task success and user satisfaction. To integrate new user preferences in a zero-shot manner, our proposed Text2Interaction framework invokes large language models to generate a task plan, motion preferences as Python code, and parameters of a safe controller. By maximizing the combined probability of task completion and user satisfaction instead of a weighted sum of rewards, we can reliably find plans that fulfill both requirements. We find that 83% of users working with Text2Interaction agree that it integrates their preferences into the robot's plan, and 94% prefer Text2Interaction over the baseline. Our ablation study shows that Text2Interaction aligns better with unseen preferences than other baselines while maintaining a high success rate.<|reference_end|>
@article{thumm2024text2interaction:,
    title={Text2Interaction: Establishing Safe and Preferable Human-Robot
  Interaction},
    author={Jakob Thumm, Christopher Agia, Marco Pavone, Matthias Althoff},
    journal={arXiv preprint arXiv:2408.06105},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06105},
    primaryClass={cs.RO}
}