Contrastive Imitation Learning for Language-Guided Multi-Task Robotic Manipulation

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.