Effective Tuning Strategies for Generalist Robot Manipulation Policies
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arXiv
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| Format: | Preprint |
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2024
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| author | Zhang, Wenbo Li, Yang Qiao, Yanyuan Huang, Siyuan Liu, Jiajun Dayoub, Feras Ma, Xiao Liu, Lingqiao |
| author_facet | Zhang, Wenbo Li, Yang Qiao, Yanyuan Huang, Siyuan Liu, Jiajun Dayoub, Feras Ma, Xiao Liu, Lingqiao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_01220 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Effective Tuning Strategies for Generalist Robot Manipulation Policies Zhang, Wenbo Li, Yang Qiao, Yanyuan Huang, Siyuan Liu, Jiajun Dayoub, Feras Ma, Xiao Liu, Lingqiao Robotics Machine Learning 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. |
| title | Effective Tuning Strategies for Generalist Robot Manipulation Policies |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2410.01220 |