長期的な両腕タスク・動作計画のための接地された視覚言語インタプリタ
Grounded Vision-Language Interpreter for Long-Horizon Bimanual Task and Motion Planning
視覚言語モデルでPDDL仕様を生成し、タスク・動作計画と修正計画を組み合わせて、検証可能な両腕ロボットの調理タスクを実現する枠組みを提案。
詳しい要約
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著者: Jeremy Siburian, Keisuke Shirai, Cristian C. Beltran-Hernandez, Masashi Hamaya, Michael Görner, Atsushi Hashimoto
分類: cs.RO, cs.AI
原文アブストラクト
While recent advances in vision-language models have accelerated language-guided robot planning, their black-box nature lacks the safety guarantees and interpretability crucial for real-world deployment. Conversely, classical symbolic planners offer rigorous safety verification but require significant expert knowledge for setup. Moreover, most existing methods are limited to single-arm pick-and-place tasks, leaving bimanual manipulation largely underexplored, despite its tightly interdependent subtasks and the need to explicitly manage inter-arm collisions. To bridge this gap, this paper proposes ViLaIn-TAMP, a hybrid planning framework for enabling verifiable, interpretable, and autonomous bimanual robot behaviors. ViLaIn-TAMP comprises three main components: (1) a Vision-Language Interpreter (ViLaIn) adapted from a prior work that converts multimodal inputs into structured PDDL problem specifications, (2) an integrated Task and Motion Planning (TAMP) system that grounds these specifications in actionable trajectory sequences through symbolic and geometric constraint reasoning, explicitly verifying feasibility before execution, and (3) a corrective planning (CP) module which receives structured motion failure feedback and feeds it as constraints back to ViLaIn to refine the specification. We design challenging bimanual manipulation tasks in a cooking domain to evaluate our framework, where experimental results show that ViLaIn-TAMP outperforms a VLM-as-a-planner baseline by 17.5% in mean success rate, with the CP module boosting it further by 32.9%. We further validate ViLaIn-TAMP on a physical dual-arm robotic system. Project page: https://omron-sinicx.github.io/ViLaIn-TAMP
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