言語条件付きロボット模倣学習で重要な要素は何か：非構造データからの検討
What Matters in Language Conditioned Robotic Imitation Learning over Unstructured Data
非構造なオフライン模倣データから言語条件付き方策を学ぶ際の設計選択を体系的に検討し、階層分解やマルチモーダルTransformer、離散潜在プラン、対照損失を組み合わせてCALVINベンチマークで最先端を上回った。
著者: Oier Mees, Lukas Hermann, Wolfram Burgard
分類: cs.RO, cs.AI, cs.CL, cs.CV
原文アブストラクト
A long-standing goal in robotics is to build robots that can perform a wide range of daily tasks from perceptions obtained with their onboard sensors and specified only via natural language. While recently substantial advances have been achieved in language-driven robotics by leveraging end-to-end learning from pixels, there is no clear and well-understood process for making various design choices due to the underlying variation in setups. In this paper, we conduct an extensive study of the most critical challenges in learning language conditioned policies from offline free-form imitation datasets. We further identify architectural and algorithmic techniques that improve performance, such as a hierarchical decomposition of the robot control learning, a multimodal transformer encoder, discrete latent plans and a self-supervised contrastive loss that aligns video and language representations. By combining the results of our investigation with our improved model components, we are able to present a novel approach that significantly outperforms the state of the art on the challenging language conditioned long-horizon robot manipulation CALVIN benchmark. We have open-sourced our implementation to facilitate future research in learning to perform many complex manipulation skills in a row specified with natural language. Codebase and trained models available at http://hulc.cs.uni-freiburg.de
