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

TL;DR

This paper introduces Sigma-Agent, a novel imitation learning framework for multi-task robotic manipulation guided by language and vision, utilizing contrastive learning and a multi-view transformer to improve task understanding and performance.

Contribution

The paper presents Sigma-Agent, combining contrastive imitation learning modules and a multi-view transformer for enhanced multi-task robotic manipulation from language and visual inputs.

Findings

Outperforms state-of-the-art methods on 18 RLBench tasks.

Achieves 62% success rate in real-world manipulation with a single policy.

Surpasses RVT by 5.2% and 5.9% in training efficiency.

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 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.