Michal Pliska presents Single-grasp Deformable Object Discrimination
On 2025-04-24 11:00:00 at E112, Karlovo náměstí 13, Praha 2
Please note that we had to reschedule this seminar from March 11. The new date
is April 24.
Article:
Pliska, M., Patni, S. P., Mares, M., Stoudek, P., Straka, Z., Stepanova, K. and
Hoffmann, M. (2024), 'Single-grasp deformable object discrimination: the effect
of gripper morphology, sensing modalities, and action parameters', IEEE
Transactions on Robotics 40, 4414 - 4426. DOI:
https://doi.org/10.1109/TRO.2024.3463402
https://youtu.be/-6cmQrSzbCs?si=BuQOd-T8r21h4y6g
Abstract:
In haptic object discrimination, the effect of gripper embodiment, action
parameters, and sensory channels has not been systematically studied. We used
two anthropomorphic hands and two 2-finger grippers to grasp two sets of
deformable objects. On the object classification task, we found: (i) among
classifiers, SVM on sensory features and LSTM on raw time series performed best
across all grippers; (ii) faster compression speeds degraded performance; (iii)
generalization to different grasping configurations was limited; transfer to
different compression speeds worked well for the Barrett Hand only.
Visualization of the feature spaces using PCA showed that gripper morphology
and
action parameters were the main source of variance, making generalization
across
embodiment or grip configurations very difficult. On the highly challenging
dataset consisting of polyurethane foams alone, only the Barrett Hand achieved
excellent performance. Tactile sensors can thus provide a key advantage even if
recognition is based on stiffness rather than shape. The data set with 24,000
measurements is publicly available.
is April 24.
Article:
Pliska, M., Patni, S. P., Mares, M., Stoudek, P., Straka, Z., Stepanova, K. and
Hoffmann, M. (2024), 'Single-grasp deformable object discrimination: the effect
of gripper morphology, sensing modalities, and action parameters', IEEE
Transactions on Robotics 40, 4414 - 4426. DOI:
https://doi.org/10.1109/TRO.2024.3463402
https://youtu.be/-6cmQrSzbCs?si=BuQOd-T8r21h4y6g
Abstract:
In haptic object discrimination, the effect of gripper embodiment, action
parameters, and sensory channels has not been systematically studied. We used
two anthropomorphic hands and two 2-finger grippers to grasp two sets of
deformable objects. On the object classification task, we found: (i) among
classifiers, SVM on sensory features and LSTM on raw time series performed best
across all grippers; (ii) faster compression speeds degraded performance; (iii)
generalization to different grasping configurations was limited; transfer to
different compression speeds worked well for the Barrett Hand only.
Visualization of the feature spaces using PCA showed that gripper morphology
and
action parameters were the main source of variance, making generalization
across
embodiment or grip configurations very difficult. On the highly challenging
dataset consisting of polyurethane foams alone, only the Barrett Hand achieved
excellent performance. Tactile sensors can thus provide a key advantage even if
recognition is based on stiffness rather than shape. The data set with 24,000
measurements is publicly available.