搜索、落地、规划：不完整场景知识下任务与运动规划的功能充分性

中文摘要 AI 辅助

基础模型（FMs）已将任务与运动规划（TAMP）扩展到通过语言和视觉观察指定的操作问题。然而，不完整的场景知识在理解任务需求与了解物理场景是否实际能够实现该需求之间留下了关键差距。我们提出了GRAB-TAMP，一个基于FM的TAMP框架，该框架搜索任务完成所需的场景实体，将功能角色落地到有效的物理对象上，并且仅在完整的联合分配建立功能充分性之后才进行规划。我们通过功能角色、关系和分配约束来表示任务，并在需求未解决时逐步检查场景，通过语义、几何和关系检查来验证候选对象。我们在涵盖厨房、客厅和工作间领域的32种场景变体上评估了GRAB-TAMP。在200次可行试验中，我们的方法实现了54.0%的端到端成功率，以及67.3%的计划目标覆盖率。与相同执行设置下的三个基于FM的TAMP框架相比，GRAB-TAMP在端到端成功率上比平均基线提高了25.7个百分点。实现和评估代码：此https URL

英文摘要

Foundation models (FMs) have expanded task and motion planning (TAMP) to manipulation problems specified through language and visual observations. However, incomplete scene knowledge leaves a critical gap between understanding what the task requires and knowing whether the physical scene can actually realize it. We introduce GRAB-TAMP, an FM-based TAMP framework that searches for scene entities required for task completion, grounds functional roles to valid physical objects, and plans only after a complete joint assignment establishes functional sufficiency. We represent the task through functional roles, relations, and assignment constraints, and incrementally inspect the scene while requirements remain unresolved, verifying candidate objects through semantic, geometric, and relational checks. We evaluate GRAB-TAMP across 32 scene variants spanning Kitchen, Living Room, and Workshop domains. Across 200 feasible trials, our approach achieves 54.0% end-to-end success with 67.3% plan goal coverage. Compared with three FM-based TAMP frameworks under the same execution setting, GRAB-TAMP improves end-to-end success by 25.7 percentage points over the mean baseline. Implementation and evaluation code: https://github.com/Narendhiranv04/GRAB-TAMP

发表机构

• Robotics Research Center, IIIT Hyderabad（海得拉巴国际信息技术学院机器人研究中心）
• Center for Security, Theory and Algorithmic Research, IIIT Hyderabad（海得拉巴国际信息技术学院安全、理论与算法研究中心）

机构由 AI 辅助整理，请以论文原文为准。

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8 pages, 4 figures, 4 tables