2018/06/03 by Balakumar Sundaralingam, Sundaralingam, Balakumar, Tucker Hermans +1 · 3 citations
Computer Science · Engineering · #FOS: Computer and information sciences #Hand Gesture Recognition Systems #Human Pose and Action Recognition #Robot Manipulation and Learning #Robotic Path Planning Algorithms #Robotics (cs.RO) #cs.RO
paper · pdf · doi:10.48550/arxiv.1806.00942
Accepted draft to Autonomous Robots
openalex publication_date 2018/06/03 · arxiv created 2018/06/09 · arxiv updated 2018/06/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper proposes a novel approach to performing in-grasp manipulation: the problem of moving an object with reference to the palm from an initial pose to a goal pose without breaking or making contacts. Our method to perform in-grasp manipulation uses kinematic trajectory optimization which requires no knowledge of dynamic properties of the object. We implement our approach on an Allegro robot hand and perform thorough experiments on 10 objects from the YCB dataset. However, the proposed method is general enough to generate motions for most objects the robot can grasp. Experimental result support the feasibillty of its application across a variety of object shapes. We explore the adaptability of our approach to additional task requirements by including collision avoidance and joint space smoothness costs. The grasped object avoids collisions with the environment by the use of a signed distance cost function. We reduce the effects of unmodeled object dynamics by requiring smooth joint trajectories. We additionally compensate for errors encountered during trajectory execution by formulating an object pose feedback controller.