arXiv cs.AI· Tsunehiko Tanaka, Matthew Stephenson, Alistair Macvicar, Edgar Simo-Serra·· 14 小时前AI 评分32
基于视觉语言模型的证据门控任务与运动规划（EAFG）
Evidence-Gated Task and Motion Planning with Vision-Language Models
AI 导读
研究提出证据获取与可行性门控（EAFG）框架，让 VLM 生成子目标、机器人执行 TAMP 规划的动作序列以获取视觉证据，再据此决定继续任务规划、补充证据还是中止。在三个烹饪场景测试中，EAFG 在明确指定与欠指定指令场景下任务完成率优于 VLM-TAMP 基线，并在所需物体缺失时能正确中止。
正文 · 原文
Abstract:Robots executing long-horizon manipulation tasks from natural-language instructions must reason about both semantic task structure and geometric feasibility. Hierarchical approaches combining Vision-Language Models (VLMs) with Task and Motion Planning (TAMP) offer a promising solution, but VLM-generated subgoals may rely on prior knowledge without sufficient observational support. This can lead to the omission of task-relevant subgoals or attempts to execute instructions whose requirements cannot be met in the environment. We propose Evidence Acquisition and Feasibility Gating (EAFG), a framework for evidence-gated task and motion planning that acquires visual evidence to specify appropriate task plans and assess whether given instructions can be fulfilled. EAFG first acquires evidence by having a VLM generate subgoals and a robot execute action sequences planned by TAMP for these subgoals. For feasibility gating, the acquired evidence is used to decide whether to proceed with task planning, acquire further evidence, or halt. When proceeding, the VLM generates task subgoals conditioned on the acquired evidence, and TAMP computes low-level actions. We evaluate EAFG in three cooking scenarios involving explicitly specified required objects, underspecified instructions, and instructions requiring an absent object. EAFG improves task completion over a VLM-TAMP baseline in the explicit and underspecified scenarios and enables appropriate halting when a required object is absent.
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.20084 [cs.RO] |
| (or arXiv:2608.20084v2 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2608.20084 arXiv-issued DOI via DataCite |
Submission history
From: Tsunehiko Tanaka [view email]
[v1]
Thu, 20 Aug 2026 14:17:52 UTC (620 KB)
[v2]
Thu, 29 Oct 2026 15:37:45 UTC (1,034 KB)
来源：arXiv cs.AI · arxiv.org