场景无关的双臂任务规划 via 视觉可及性推理
Scene-agnostic Hierarchical Bimanual Task Planning via Visual Affordance Reasoning
AI总结：
本文提出了一种场景无关的双臂任务规划框架，通过视觉可及性推理实现双臂操作的高效规划与执行。
摘要：
在开放环境中运作的具身智能体必须将高层指令转化为可执行的行为，通常需要协调使用双手。尽管最近的基模型提供了强大的语义推理能力，但现有的机器人任务规划器仍然主要是单手的，并未能解决在场景无关设置中双臂操作固有的空间、几何和协调挑战。我们提出了一种统一的框架，用于场景无关的双臂任务规划，该框架将高层推理与3D基础的双手执行连接起来。我们的方法集成了三个关键模块。视觉点定位（VPG）分析单个场景图像以检测相关物体并生成世界对齐的交互点。双臂子目标规划器（BSP）在空间相邻性和跨物体可达性上进行推理，以生成紧凑的、运动中性化的子目标，这些子目标利用协调双臂动作的机会。交互点驱动的双臂提示（IPBP）将这些子目标与结构化的技能库绑定，实例化满足手状态和可及性约束的同步单手或双手动作序列。这些模块共同使智能体能够在杂乱、之前未见过的场景中规划语义有意义、物理可行且可并行化的双手行为。实验表明，它能产生连贯、可行且紧凑的双手计划，并且能够在不重新训练的情况下泛化到杂乱场景，展示了双手任务的鲁棒场景无关可及性推理。
英文摘要：
Embodied agents operating in open environments must translate high-level instructions into grounded, executable behaviors, often requiring coordinated use of both hands. While recent foundation models offer strong semantic reasoning, existing robotic task planners remain predominantly unimanual and fail to address the spatial, geometric, and coordination challenges inherent to bimanual manipulation in scene-agnostic settings. We present a unified framework for scene-agnostic bimanual task planning that bridges high-level reasoning with 3D-grounded two-handed execution. Our approach integrates three key modules. Visual Point Grounding (VPG) analyzes a single scene image to detect relevant objects and generate world-aligned interaction points. Bimanual Subgoal Planner (BSP) reasons over spatial adjacency and cross-object accessibility to produce compact, motion-neutralized subgoals that exploit opportunities for coordinated two-handed actions. Interaction-Point-Driven Bimanual Prompting (IPBP) binds these subgoals to a structured skill library, instantiating synchronized unimanual or bimanual action sequences that satisfy hand-state and affordance constraints. Together, these modules enable agents to plan semantically meaningful, physically feasible, and parallelizable two-handed behaviors in cluttered, previously unseen scenes. Experiments show that it produces coherent, feasible, and compact two-handed plans, and generalizes to cluttered scenes without retraining, demonstrating robust scene-agnostic affordance reasoning for bimanual tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
