@inproceedings{
cho2024metacontroller,
title={Meta-Controller: Few-Shot Imitation of Unseen Embodiments and Tasks in Continuous Control},
author={Seongwoong Cho and Donggyun Kim and Jinwoo Lee and Seunghoon Hong},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=M5D5rMwLjj}
}
Generalizing across robot embodiments and tasks is crucial for adaptive robotic systems. Modular policy learning approaches adapt to new embodiments but are limited to specific tasks, while few-shot imitation learning (IL) approaches often focus on a single embodiment.
In this paper, we introduce a few-shot behavior cloning framework to simultaneously generalize to unseen embodiments and tasks using a few (e.g., five) reward-free demonstrations. Our framework leverages a joint-level input-output representation to unify the state and action spaces of heterogeneous embodiments and employs a novel structure-motion state encoder that is parameterized to capture both shared knowledge across all embodiments and embodiment-specific knowledge. A matching-based policy network then predicts actions from a few demonstrations, producing an adaptive policy that is robust to over-fitting. Evaluated in the DeepMind Control suite, our framework termed Meta-Controller demonstrates superior few-shot generalization to unseen embodiments and tasks over modular policy learning and few-shot IL approaches.
Meta-Controller: Few-Shot Imitation of Unseen Embodiments and Tasks in Continuous Control
Seongwoong Cho Donggyun Kim Jinwoo Lee Seunghoon Hong