Estimating Tactile Data for Adaptive Grasping of Novel Objects
This work addresses stable robotic grasping of unfamiliar objects, representing an incremental improvement in domain-specific manipulation.
The paper tackles the problem of adaptive grasping for novel objects by simulating tactile data to evaluate and improve grasp stability, achieving an 88% success rate on the YCB object set.
We present an adaptive grasping method that finds stable grasps on novel objects. The main contributions of this paper is in the computation of the probability of success of grasps in the vicinity of an already applied grasp. Our method performs grasp adaptions by simulating tactile data for grasps in the vicinity of the current grasp. The simulated data is used to evaluate hypothetical grasps and thereby guide us toward better grasps. We demonstrate the applicability of our method by constructing a system that can plan, apply and adapt grasps on novel objects. Experiments are conducted on objects from the YCB object set and the success rate of our method is 88%. Our experiments show that the application of our grasp adaption method improves grasp stability significantly.