用廉价几何标签训练模型，快速筛选机械臂抓取放置的可行方案。
Optimizing Robotic Placement via Grasp-Dependent Feasibility Prediction
- 基于物体姿态和抓取点，生成路径感知的可行性与碰撞标签。
- 在固定预算下，减少规划调用次数，成功率达基准水平以上。
- 适合资源受限场景下的机器人抓放任务，尤其关注效率优化。
本文研究是否可通过低成本、无需物理模拟的监督信号，有效优先排序预算敏感的抓取-放置候选方案。从物体初始位姿、目标位姿及候选抓取点出发，构建两个路径感知的几何标签：沿固定预设路径点模板的逆运动学（IK）可行性，以及基于网格扫掠的穿越碰撞标志。一个轻量级双输出MLP从位姿编码中学习这些信号；测试时，其得分用于对预先计算的候选方案排序，配合相同IK约束与规划器执行‘先排序后规划’策略。尽管仅使用廉价标签训练，该模型得分仍可迁移至含物理模拟的真实轨迹：在固定规划预算下，策略能更早找到成功路径，减少规划调用次数，且最终成功率保持持平或更高。本工作聚焦单个刚性立方体、侧面抓取及固定路径点模板，同时提出扩展至多样化物体与更复杂路径模板的思路。
原文摘要 · Abstract (English)
In this paper, we study whether inexpensive, physics-free supervision can reliably prioritize grasp-place candidates for budget-aware pick-and-place. From an object's initial pose, target pose, and a candidate grasp, we generate two path-aware geometric labels: path-wise inverse kinematics (IK) feasibility across a fixed approach-grasp-lift waypoint template, and a transit collision flag from mesh sweeps along the same template. A compact dual-output MLP learns these signals from pose encodings, and at test time its scores rank precomputed candidates for a rank-then-plan policy under the same IK gate and planner as the baseline. Although learned from cheap labels only, the scores transfer to physics-enabled executed trajectories: at a fixed planning budget the policy finds successful paths sooner with fewer planner calls while keeping final success on par or better. This work targets a single rigid cuboid with side-face grasps and a fixed waypoint template, and we outline extensions to varied objects and richer waypoint schemes.
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