制約付き自然言語行動計画による堅牢な身体性システム
Constrained Natural Language Action Planning for Resilient Embodied Systems
LLMプランナーに記号計画の監視を組み合わせ、ハルシネーションを抑えつつオープンワールドでの汎化性能を維持する新しいロボット計画手法を提案。ALFWorldで99%の成功率、実機四足歩行ロボットで100%のタスク成功率を達成した。
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2. 先行研究と比べてどこがすごい？
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著者: Grayson Byrd, Corban Rivera, Bethany Kemp, Meghan Booker, Aurora Schmidt, Celso M de Melo, Lalithkumar Seenivasan, Mathias Unberath
分類: cs.RO, cs.AI
原文アブストラクト
Replicating human-level intelligence in the execution of embodied tasks remains challenging due to the unconstrained nature of real-world environments. Novel use of large language models (LLMs) for task planning seeks to address the previously intractable state/action space of complex planning tasks, but hallucinations limit their reliability, and thus, viability beyond a research context. Additionally, the prompt engineering required to achieve adequate system performance lacks transparency, and thus, repeatability. In contrast to LLM planning, symbolic planning methods offer strong reliability and repeatability guarantees, but struggle to scale to the complexity and ambiguity of real-world tasks. We introduce a new robotic planning method that augments LLM planners with symbolic planning oversight to improve reliability and repeatability, and provide a transparent approach to defining hard constraints with considerably stronger clarity than traditional prompt engineering. Importantly, these augmentations preserve the reasoning capabilities of LLMs and retain impressive generalization in open-world environments. We demonstrate our approach in simulated and real-world environments. On the ALFWorld planning benchmark, our approach outperforms current state-of-the-art methods, achieving a near-perfect 99% success rate. Deployment of our method to a real-world quadruped robot resulted in 100% task success compared to 50% and 30% for pure LLM and symbolic planners across embodied pick and place tasks. Our approach presents an effective strategy to enhance the reliability, repeatability and transparency of LLM-based robot planners while retaining their key strengths: flexibility and generalizability to complex real-world environments. We hope that this work will contribute to the broad goal of building resilient embodied intelligent systems.
