SRSA: Skill Retrieval and Adaptation for Robotic Assembly Tasks
Journal:
arXiv
Published Date:
Mar 6, 2025
Abstract
Enabling robots to learn novel tasks in a data-efficient manner is a
long-standing challenge. Common strategies involve carefully leveraging prior
experiences, especially transition data collected on related tasks. Although
much progress has been made for general pick-and-place manipulation, far fewer
studies have investigated contact-rich assembly tasks, where precise control is
essential. We introduce SRSA (Skill Retrieval and Skill Adaptation), a novel
framework designed to address this problem by utilizing a pre-existing skill
library containing policies for diverse assembly tasks. The challenge lies in
identifying which skill from the library is most relevant for fine-tuning on a
new task. Our key hypothesis is that skills showing higher zero-shot success
rates on a new task are better suited for rapid and effective fine-tuning on
that task. To this end, we propose to predict the transfer success for all
skills in the skill library on a novel task, and then use this prediction to
guide the skill retrieval process. We establish a framework that jointly
captures features of object geometry, physical dynamics, and expert actions to
represent the tasks, allowing us to efficiently learn the transfer success
predictor. Extensive experiments demonstrate that SRSA significantly
outperforms the leading baseline. When retrieving and fine-tuning skills on
unseen tasks, SRSA achieves a 19% relative improvement in success rate,
exhibits 2.6x lower standard deviation across random seeds, and requires 2.4x
fewer transition samples to reach a satisfactory success rate, compared to the
baseline. Furthermore, policies trained with SRSA in simulation achieve a 90%
mean success rate when deployed in the real world. Please visit our project
webpage https://srsa2024.github.io/.
