Contrastive Imitation Learning for Language-guided Multi-Task Robotic Manipulation
Teli Ma, Jiaming Zhou, Zifan Wang, Ronghe Qiu, Junwei Liang
Abstract
Developing robots capable of executing various manipulation tasks, guided by natural language instructions and visual observations of intricate real-world environments, remains a significant challenge in robotics. Such robot agents need to understand linguistic commands and distinguish between the requirements of different tasks. In this work, we present $\mathtt{\Sigma\mbox{-}agent}$, an end-to-end imitation learning agent for multi-task robotic manipulation. $\mathtt{\Sigma\mbox{-}agent}$ incorporates contrastive Imitation Learning (contrastive IL) modules to strengthen vision-language and current-future representations. An effective and efficient multi-view querying Transformer (MVQ-Former) for aggregating representative semantic information is introduced. $\mathtt{\Sigma\mbox{-}agent}$ shows substantial improvement over state-of-the-art methods under diverse settings in 18 RLBench tasks, surpassing RVT by an average of 5.2% and 5.9% in 10 and 100 demonstration training, respectively. $\mathtt{\Sigma\mbox{-}agent}$ also achieves 62% success rate with a single policy in 5 real-world manipulation tasks. The code will be released upon acceptance.
BibTeX
@inproceedings{
ma2024contrastive,
title={Contrastive Imitation Learning for Language-guided Multi-Task Robotic Manipulation},
author={Teli Ma and Jiaming Zhou and Zifan Wang and Ronghe Qiu and Junwei Liang},
booktitle={8th Annual Conference on Robot Learning},
year={2024},
url={https://openreview.net/forum?id=9HkElMlPbU}
}