无需训练的机器人规划系统，用大模型生成可执行动作树，提升复杂操作成功率。
Sequential Planning via Anchored Robotic Keypoints
- 用大模型生成带类型的动作树，整合低层控制指令，避免重复计算。
- 在LIBERO-PRO上达43.7%成功率，比基线高出一倍以上，空间任务提升27.7点。
- 支持多机器人平台，失败可自动重试并定位问题模块，适合无监督数据收集。
我们提出无需训练的神经符号操纵系统SPARK，通过单次Gemini调用生成带类型的动作树（BT），包含可组合的底层控制（运动、抓取、深度几何）。感知模块使用第二次Gemini调用为每个物体生成三个备选提示，SAM3评估后选择置信度最高的提示-标签对。若某动作失败，恢复循环会重新检测物体并重试，无需额外大模型调用。该设计使空间任务提升27.7点，对象任务提升10.0点，整体增益5.0点。SPARK在三种机器人（UR10e、Franka FR3、双臂Franka）上完成九项任务，每项20次试验，平均成功率达68%。由于检测器、规划器与控制器模块独立可替换，且每个动作有可验证的后条件，能精准定位故障来源。所有试验均生成带标签的可验证轨迹，为无需远程操控的强化学习提供高质量数据。
原文摘要 · Abstract (English)
We present Sequential Planning via Anchored Robotic Keypoints, SPARK, a training-free neurosymbolic manipulation system that reaches 43.7% on six LIBERO-PRO position \& task cells, more than doubling CaP-Agent0 and Vision-Language-Action (VLA) baselines. CaP-Agent0, a multi-turn code-generation agent, achieves 18.2% by re-querying an LLM at every turn, but its restart-from-scratch solution proves costly against minor policy failures. Perception is the layer that fails most under position and task changes so SPARK spends its computation there. A single Gemini call composes the plan as a typed behavior tree (BT) of composable primitives, each already containing the low-level control (motion, grasping, depth geometry) a code-generation agent would otherwise regenerate on every trial. The rest of the budget goes to perception: a second Gemini call proposes three alternative text prompts per object, SAM3 evaluates each, and we keep the prompt$\to$label pair with the most confident detection and a recovery loop then retries a failed primitive against freshly detected objects, with no new LLM call. The alternative prompts add +27.7 points on the spatial suite and +10.0 on the object suite, with the recovery loop adding +5.0 overall. SPARK runs the same primitives on three robot families (UR10e, Franka FR3, bimanual Franka) across nine unique tasks at twenty trials each, averaging 68%. Since the detector, planner, and controller modules sit behind the typed plan, they swap independently without training, and each primitive's checkable post-condition traces a failure to the corresponding module or a kinematic limit. Every trial logs a verified, labeled trajectory, so a training-free planner that already beats VLAs can supply the data those policies need without teleoperation. Project page: https://cwru-aism.github.io/spark-page/
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