言語指示に基づくマルチタスクロボット操作のための対照的模倣学習
Contrastive Imitation Learning for Language-guided Multi-Task Robotic Manipulation
言語指示と視覚観察から多様な操作タスクを実行するエンドツーエンド模倣学習エージェントSigma-Agentを提案し、対照的学習とマルチビューTransformerで性能を向上させた。
著者: Teli Ma, Jiaming Zhou, Zifan Wang, Ronghe Qiu, Junwei Liang
分類: cs.RO, cs.CV
原文アブストラクト
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.
