TrySee: 専門分野向け閉ループロボットタスク計画のためのモジュール型知覚強化VLMグラウンディング
TrySee: Modular Perception-Enhanced VLM Grounding for Closed-Loop Robot Task Planning in Specialized Domains
汎用VLMの推論とドメイン特化の知覚モジュールを組み合わせ、微調整や実演データなしで専門分野のロボットタスク計画を閉ループで行うパイプラインを提案し、実世界の分解作業を含む5分野で評価した。
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著者: Elias Hofele, Thomas Trapp, David Timmermann, Tristan Schnell, Georg Heppner, Arne Roennau
分類: cs.RO, cs.AI
原文アブストラクト
Vision Language Models (VLMs) have shown impressive capabilities in high-level reasoning and task planning for robots. However, specialized domains remain a challenge for VLMs' image interpretation and grounding abilities. We introduce TrySee, a modular closed-loop robot task planning pipeline integrating general-purpose VLM reasoning with domain-specific perception and established robotics components. The system consists of an inherently reactive cyclic planning pipeline, augmented with domain-specific modular vision preprocessing, to provide online grounding. Finally, a dynamic preprocessing selection mechanism contributes to the scalability of the pipeline. The system requires neither VLM fine-tuning nor end-to-end demonstrations and adapts through replaceable perception modules and motion planning. The approach is evaluated on planning tasks across 5 widely different domains, including a real-world robot disassembly task. The field tests in a brownfield environment demonstrate the approach's practicality in integrating VLM planning into specialized robotic systems with limited domain-specific training data.