TY - GEN
T1 - A Novel Simulation-Based Quality Metric for Evaluating Grasps on 3D Deformable Objects
AU - Le, Tran Nguyen
AU - Lundell, Jens
AU - Abu-Dakka, Fares J.
AU - Kyrki, Ville
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Evaluation of grasps on deformable 3D objects is a little-studied problem, even if the applicability of rigid object grasp quality measures for deformable ones is an open question. A central issue with most quality measures is their dependence on contact points, which for deformable objects depend on the deformations. This paper proposes a grasp quality measure for deformable objects that uses information about object deformation to calculate the grasp quality. Grasps are evaluated by simulating the deformations during grasping and predicting the contacts between the gripper and the grasped object. The contact information is then used as input for a new grasp quality metric to quantify the grasp quality. The approach is benchmarked against two classical rigid-body quality metrics on over 600 grasps in the Isaac gym simulation and over 50 real-world grasps. Experimental results show an average improvement of 18% in the grasp success rate for deformable objects compared to the classical rigid-body quality metrics. Furthermore, the proposed approach is approximately fifteen times faster to calculate than the shake task, which, to date, is one of the most reliable approaches to quantify a grasp on a deformable object.
AB - Evaluation of grasps on deformable 3D objects is a little-studied problem, even if the applicability of rigid object grasp quality measures for deformable ones is an open question. A central issue with most quality measures is their dependence on contact points, which for deformable objects depend on the deformations. This paper proposes a grasp quality measure for deformable objects that uses information about object deformation to calculate the grasp quality. Grasps are evaluated by simulating the deformations during grasping and predicting the contacts between the gripper and the grasped object. The contact information is then used as input for a new grasp quality metric to quantify the grasp quality. The approach is benchmarked against two classical rigid-body quality metrics on over 600 grasps in the Isaac gym simulation and over 50 real-world grasps. Experimental results show an average improvement of 18% in the grasp success rate for deformable objects compared to the classical rigid-body quality metrics. Furthermore, the proposed approach is approximately fifteen times faster to calculate than the shake task, which, to date, is one of the most reliable approaches to quantify a grasp on a deformable object.
UR - http://www.scopus.com/inward/record.url?scp=85146351855&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146351855&partnerID=8YFLogxK
U2 - 10.1109/IROS47612.2022.9981169
DO - 10.1109/IROS47612.2022.9981169
M3 - Conference contribution
AN - SCOPUS:85146351855
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 3123
EP - 3129
BT - IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
Y2 - 23 October 2022 through 27 October 2022
ER -