外观
论文arXiv 2609.199461 个月前
面向抓取条件化机器人放置的执行感知预执行排序
原文链接：Execution-Aware Pre-Execution Ranking for Grasp-Conditioned Robotic Placement（arXiv 2609.19946 · 发布日期：2026-09-17）
Execution-Aware Pre-Execution Ranking for Grasp-Conditioned Robotic Placement
该工作将放置建模为预执行排序，结合点云与位姿描述符，在30物体、1235场景数据和xArm7/MoveIt迁移上验证。
核心要点
- 几何有效放置会因抓取改变末端位姿、碰撞几何和搬运动作而难以执行。
- 模型融合类型化目标条件点云与三种位姿描述符，层级预测规划与执行成功。
- 场景组留出测试中，三种子Top-1成功率联合选择达85.63%，固定目标达79.84%。
- 冻结迁移到xArm7/MoveIt无需特定重训练，27个锁定案例中13个端到端完成。
验证范围与局限：验证范围包括30物体、1235场景数据集，以及xArm7/MoveIt上的27个锁定迁移案例。
英文摘要 (Abstract)
A geometrically valid placement can still be difficult to execute because the selected grasp changes the required end-effector pose, collision geometry, and transport motion. Placement is formulated as a pre-execution ranking problem in which supplied grasp-placement candidates are scored before planning. The model combines a typed target-conditioned point cloud with three pose descriptors and hierarchical heads for planning success and execution success conditioned on planning. On a 30-object, 1,235-scene dataset with scene-group-held-out splits, three-seed top-1 success on covered test groups reaches 85.63 +/- 1.08% for joint selection and 79.84 +/- 0.16% for fixed-target ranking. For the designated frozen seed-42 checkpoint, top-1 success improves from 72.84% to 85.78% over full-pool cuMotion for joint ranking and from 59.65% to 79.67% for fixed-target ranking. Frozen transfer to xArm7/MoveIt requires no xArm-specific retraining. Across 27 locked cases, 13 complete end to end (48.15%). Of the 16 cases that pass Top-5 preflight and begin execution, 13 succeed (81.25%). Candidate-level deployment-feasibility prediction reaches 81.25% recall, 85.20% specificity, and 83.23% balanced accuracy.
作者：Tianyuan Liu, Rutherford Agbeshi Patamia, Benjamin T. Champion, Richard Dazeley, Akansel Cosgun
来源
本页事实对应的公开页面，以原文为准。