Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.01520 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915593490268160 |
|---|---|
| author | Lyu, Shipeng Sheng, Lijie Wang, Fangyuan Zhang, Wenyao Lin, Weiwei Jia, Zhenzhong Navarro-Alarcon, David Guo, Guodong |
| author_facet | Lyu, Shipeng Sheng, Lijie Wang, Fangyuan Zhang, Wenyao Lin, Weiwei Jia, Zhenzhong Navarro-Alarcon, David Guo, Guodong |
| contents | Humans naturally grasp objects with minimal level required force for stability, whereas robots often rely on rigid, over-squeezing control. To narrow this gap, we propose a human-inspired physics-conditioned tactile method (Phy-Tac) for force-optimal stable grasping (FOSG) that unifies pose selection, tactile prediction, and force regulation. A physics-based pose selector first identifies feasible contact regions with optimal force distribution based on surface geometry. Then, a physics-conditioned latent diffusion model (Phy-LDM) predicts the tactile imprint under FOSG target. Last, a latent-space LQR controller drives the gripper toward this tactile imprint with minimal actuation, preventing unnecessary compression. Trained on a physics-conditioned tactile dataset covering diverse objects and contact conditions, the proposed Phy-LDM achieves superior tactile prediction accuracy, while the Phy-Tac outperforms fixed-force and GraspNet-based baselines in grasp stability and force efficiency. Experiments on classical robotic platforms demonstrate force-efficient and adaptive manipulation that bridges the gap between robotic and human grasping. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_01520 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Phy-Tac: Toward Human-Like Grasping via Physics-Conditioned Tactile Goals Lyu, Shipeng Sheng, Lijie Wang, Fangyuan Zhang, Wenyao Lin, Weiwei Jia, Zhenzhong Navarro-Alarcon, David Guo, Guodong Robotics Humans naturally grasp objects with minimal level required force for stability, whereas robots often rely on rigid, over-squeezing control. To narrow this gap, we propose a human-inspired physics-conditioned tactile method (Phy-Tac) for force-optimal stable grasping (FOSG) that unifies pose selection, tactile prediction, and force regulation. A physics-based pose selector first identifies feasible contact regions with optimal force distribution based on surface geometry. Then, a physics-conditioned latent diffusion model (Phy-LDM) predicts the tactile imprint under FOSG target. Last, a latent-space LQR controller drives the gripper toward this tactile imprint with minimal actuation, preventing unnecessary compression. Trained on a physics-conditioned tactile dataset covering diverse objects and contact conditions, the proposed Phy-LDM achieves superior tactile prediction accuracy, while the Phy-Tac outperforms fixed-force and GraspNet-based baselines in grasp stability and force efficiency. Experiments on classical robotic platforms demonstrate force-efficient and adaptive manipulation that bridges the gap between robotic and human grasping. |
| title | Phy-Tac: Toward Human-Like Grasping via Physics-Conditioned Tactile Goals |
| topic | Robotics |
| url | https://arxiv.org/abs/2511.01520 |