发表机构
University of Southern Denmark（南丹麦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对机器人插入中抓取后运动学不可行问题，提出通过夹爪内物体重定向修复，利用分支感知IK图和任务条件先验优化，仿真与真实实验验证有效。
AI 中文摘要
稳定的抓取并不能保证运动学上可行的机器人插入，因为夹爪内物体变换可能迫使机器人沿预定插入路径接近奇异点或关节极限。我们研究通过夹爪内物体重定向进行抓取后运动学可行性修复。给定已实现的抓取和固定插入路径，我们寻求一个小的重定向以恢复运动学可行性。顺序IK可能因遵循不利的关节空间路径而错过此类候选，而非光滑的可行性景观使搜索计算成本高昂。我们使用分支感知IK图评估候选，该图在离散插入路径上最大化最小可行性裕度，并利用学习的任务条件先验改进查询排序。选定的重定向通过基于触觉的外在操作执行。在UR5e仿真中，无学习排序的规划器相比基于网格的顺序IK，在成功试验中将平均重定向减少51.5%。添加学习排序后，该规划器的平均规划时间额外减少51.1%。真实机器人实验验证了完整流程。
英文摘要
A stable grasp does not guarantee kinematically feasible robotic insertion because the object-in-gripper transform may force the robot towards singularities or joint limits along the prescribed insertion path. We study post-grasp kinematic feasibility repair through object-in-gripper reorientation. Given an achieved grasp and a fixed insertion path, we seek a small reorientation that restores kinematic feasibility. Sequential IK can miss such candidates by following an unfavorable joint-space path, while the nonsmooth feasibility landscape makes the search computationally expensive. We evaluate candidates using a branch-aware IK graph that maximizes the minimum feasibility margin over the discretized insertion path and use a learned task-conditioned prior to improve query ordering. The selected reorientation is executed through tactile-based extrinsic manipulation. In UR5e simulations, the planner without learned ranking reduces mean reorientation over successful trials by 51.5% compared with grid-based sequential IK. Adding learned ranking reduces this planner's mean planning time by an additional 51.1%. Real-robot experiments validate the complete pipeline.
