Joint Observation of Object Pose and Tactile Imprints for Online Grasp Stability Assessment
2011 (English)Conference paper (Refereed)
This paper studies the viability of concurrentobject pose tracking and tactile sensing for assessing graspstability on a physical robotic platform. We present a kernellogistic-regression model of pose- and touch-conditional graspsuccess probability. Models are trained on grasp data whichconsist of (1) the pose of the gripper relative to the object,(2) a tactile description of the contacts between the objectand the fully-closed gripper, and (3) a binary descriptionof grasp feasibility, which indicates whether the grasp canbe used to rigidly control the object. The data is collectedby executing grasps demonstrated by a human on a roboticplatform composed of an industrial arm, a three-finger gripperequipped with tactile sensing arrays, and a vision-based objectpose tracking system. The robot is able to track the poseof an object while it is grasping it, and it can acquiregrasp tactile imprints via pressure sensor arrays mounted onits gripper’s fingers. We consider models defined on severalsubspaces of our input data – using tactile perceptions orgripper poses only. Models are optimized and evaluated with f-fold cross-validation. Our preliminary results show that stabilityassessments based on both tactile and pose data can providebetter rates than assessments based on tactile data alone.
Place, publisher, year, edition, pages
Computer Science Robotics
IdentifiersURN: urn:nbn:se:kth:diva-63799OAI: oai:DiVA.org:kth-63799DiVA: diva2:482672
IEEE ICRA 2011 workshop: Manipulation Under Uncertainty, Shanghai, China, May 13th 2011
QC 201204162012-01-242012-01-242012-04-16Bibliographically approved