CLIMB: 言語ガイドによる反復モデル構築を伴う継続的タスクプランニング学習
CLIMB: Language-Guided Continual Learning for Task Planning with Iterative Model Building
基盤モデルと実行フィードバックを活用し、自然言語記述からドメインモデルを構築しながら未知の述語を学習・蓄積する継続的タスクプランニング手法を提案。
著者: Walker Byrnes, Miroslav Bogdanovic, Avi Balakirsky, Stephen Balakirsky, Animesh Garg
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Intelligent and reliable task planning is a core capability for generalized robotics, requiring a descriptive domain representation that sufficiently models all object and state information for the scene. We present CLIMB, a continual learning framework for robot task planning that leverages foundation models and execution feedback to guide domain model construction. CLIMB can build a model from a natural language description, learn non-obvious predicates while solving tasks, and store that information for future problems. We demonstrate the ability of CLIMB to improve performance in common planning environments compared to baseline methods. We also develop the BlocksWorld++ domain, a simulated environment with an easily usable real counterpart, together with a curriculum of tasks with progressing difficulty for evaluating continual learning. Additional details and demonstrations for this system can be found at https://plan-with-climb.github.io/ .
