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 ten objects from the YCB dataset. The proposed method is general enough to generate motions for most objects the robot can grasp. Experimental results 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.
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This study was funded partly by National Science Foundation (NSF) (Grant Number 1657596).
Conflict of interest
Author Balakumar Sundaralingam has no conflicts of interest. Author Tucker Hermans has no conflicts of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
This is one of several papers published in Autonomous Robots comprising the “Special Issue on Robotics Science and Systems”.
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Sundaralingam, B., Hermans, T. Relaxed-rigidity constraints: kinematic trajectory optimization and collision avoidance for in-grasp manipulation. Auton Robot 43, 469–483 (2019). https://doi.org/10.1007/s10514-018-9772-z
- Dexterous manipulation
- Trajectory optimization
- Motion planning