Effective Tuning Strategies for Generalist Robot Manipulation Policies

TL;DR

This paper systematically investigates fine-tuning strategies for generalist robot manipulation policies, demonstrating that careful design choices significantly improve performance in low-data scenarios and establishing new baselines.

Contribution

It provides a comprehensive empirical analysis of fine-tuning strategies for GMPs, offering practical guidelines and establishing new performance benchmarks.

Findings

Careful fine-tuning strategies greatly improve GMP performance in low-data regimes.

Systematic analysis identifies key factors influencing fine-tuning success.

Fine-tuned GMPs outperform state-of-the-art imitation learning methods.

Abstract

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.