[
    {
        "id": "osp-18279",
        "type": "article-journal",
        "title": "Post-Grasp Kinematic Repair for Robotic Insertion via Object-in-Gripper Reorientation",
        "author": [
            {
                "family": "Lee",
                "given": "Haegu"
            },
            {
                "family": "Sloth",
                "given": "Christoffer"
            }
        ],
        "URL": "https://omanscience.com/en/articles/post-grasp-kinematic-repair-for-robotic-insertion-via-object-in-gripper-reorientation",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "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."
    }
]