Search, Ground, Plan: Functional Sufficiency for Task and Motion Planning under Incomplete Scene Knowledge

2026-09-19

AI Summary

该研究解决了在场景知识不完整情况下的任务和运动规划问题，通过引入GRAB-TAMP框架搜索、确认场景实体并规划，以确保功能充分性。

Abstract

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

Problem

• Task and Motion Planning
• Incomplete Scene Knowledge
• Functional Sufficiency

Innovation

• Foundation models
• Task and motion planning
• Incomplete scene knowledge
• Functional sufficiency
• GRAB-TAMP