Contrastive Imitation Learning for Language-guided Multi-Task Robotic Manipulation
<|reference_start|>Contrastive Imitation Learning for Language-guided Multi-Task Robotic Manipulation: 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 Sigma-Agent, an end-to-end imitation learning agent for multi-task robotic manipulation. Sigma-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. Sigma-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. Sigma-Agent also achieves 62% success rate with a single policy in 5 real-world manipulation tasks. The code will be released upon acceptance.<|reference_end|>
@article{ma2024contrastive,
    title={Contrastive Imitation Learning for Language-guided Multi-Task Robotic
  Manipulation},
    author={Teli Ma, Jiaming Zhou, Zifan Wang, Ronghe Qiu, Junwei Liang},
    journal={arXiv preprint arXiv:2406.09738},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.09738},
    primaryClass={cs.RO cs.CV}
}