Effective Tuning Strategies for Generalist Robot Manipulation Policies
<|reference_start|>Effective Tuning Strategies for Generalist Robot Manipulation Policies: Generalist robot manipulation policies (GMPs) have the potential to generalize across a wide range of tasks, devices, and environments. However, existing policies continue to struggle with out-of-distribution scenarios due to the inherent difficulty of collecting sufficient action data to cover extensively diverse domains. While fine-tuning offers a practical way to quickly adapt a GMPs to novel domains and tasks with limited samples, we observe that the performance of the resulting GMPs differs significantly with respect to the design choices of fine-tuning strategies. In this work, we first conduct an in-depth empirical study to investigate the effect of key factors in GMPs fine-tuning strategies, covering the action space, policy head, supervision signal and the choice of tunable parameters, where 2,500 rollouts are evaluated for a single configuration. We systematically discuss and summarize our findings and identify the key design choices, which we believe give a practical guideline for GMPs fine-tuning. We observe that in a low-data regime, with carefully chosen fine-tuning strategies, a GMPs significantly outperforms the state-of-the-art imitation learning algorithms. The results presented in this work establish a new baseline for future studies on fine-tuned GMPs, and provide a significant addition to the GMPs toolbox for the community.<|reference_end|>
@article{zhang2024effective,
    title={Effective Tuning Strategies for Generalist Robot Manipulation Policies},
    author={Wenbo Zhang, Yang Li, Yanyuan Qiao, Siyuan Huang, Jiajun Liu, Feras
  Dayoub, Xiao Ma, Lingqiao Liu},
    journal={arXiv preprint arXiv:2410.01220},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01220},
    primaryClass={cs.RO cs.LG}
}