長期言語指示実行のための持続的空間意味表現
A Persistent Spatial Semantic Representation for High-level Natural Language Instruction Execution
ロボットが高レベルな自然言語指示を長期的に実行できるよう、環境の空間的・意味的情報を持続的に保持する表現手法を提案し、ALFREDベンチマークで最高性能を達成した。
著者: Valts Blukis, Chris Paxton, Dieter Fox, Animesh Garg, Yoav Artzi
分類: cs.RO, cs.AI, cs.CL, cs.CV, cs.LG
原文アブストラクト
Natural language provides an accessible and expressive interface to specify long-term tasks for robotic agents. However, non-experts are likely to specify such tasks with high-level instructions, which abstract over specific robot actions through several layers of abstraction. We propose that key to bridging this gap between language and robot actions over long execution horizons are persistent representations. We propose a persistent spatial semantic representation method, and show how it enables building an agent that performs hierarchical reasoning to effectively execute long-term tasks. We evaluate our approach on the ALFRED benchmark and achieve state-of-the-art results, despite completely avoiding the commonly used step-by-step instructions.
