1Peking University
2Beijing Academy of Artificial Intelligence
3Galbot
† corresponding author
Synthesized task-oriented dexterous grasps. GWS is expected to cover TWS for each task.
Grasp Wrench Space (GWS) : all wrenches that can be applied on object through hand contacts.
Task Wrench Space (TWS) : all wrenches that should be applied on object during task execution. TWS is approximated as a 6D hyper-fan and given as a task prior in this work.
This work tackles the problem of task-oriented dexterous hand pose synthesis, which involves generating a static hand pose capable of applying a task-specific set of wrenches to manipulate objects. Unlike previous approaches that focus solely on force-closure grasps, which are unsuitable for non-prehensile manipulation tasks (e.g., turning a knob or pressing a button), we introduce a unified framework covering force-closure grasps, non-force-closure grasps, and a variety of non-prehensile poses. Our key idea is a novel optimization objective quantifying the disparity between the Task Wrench Space (TWS, the desired wrenches predefined as a task prior) and the Grasp Wrench Space (GWS, the achievable wrenches computed from the current hand pose). By minimizing this objective, gradient-based optimization algorithms can synthesize task-oriented hand poses without additional human demonstrations. Our specific contributions include 1) a fast, accurate, and differentiable technique for estimating the GWS boundary; 2) a task-oriented objective function based on the disparity between the estimated GWS boundary and the provided TWS boundary; and 3) an efficient implementation of the synthesis pipeline that leverages CUDA accelerations and supports largescale paralleling. Experimental results on 10 diverse tasks demonstrate a 72.6% success rate in simulation. Furthermore, real-world validation for 4 tasks confirms the effectiveness of synthesized poses for manipulation. Notably, despite being primarily tailored for task-oriented hand pose synthesis, our pipeline can generate force-closure grasps 50 times faster than DexGraspNet while maintaining comparable grasp quality.
Rotate Knob
(specify torque)
Turn Handle
(specify force)
Turn Handle
(force closure)
Grasp Toy
(force closure)
Rotate Key
Rotate Lid
Rotate Knob
Pull Closet
Drag Chair
Pinch Cable
Grasp Mug
Lift Handbag
Lift Plate
Lift Suitcase
Press Stapler
Press Button
Rotate Three-Lobed Knob
@article{chen2023task,
title={Task-Oriented Dexterous Grasp Synthesis via Differentiable Grasp Wrench Boundary Estimator},
author={Chen, Jiayi and Chen, Yuxing and Zhang, Jialiang and Wang, He},
journal={arXiv preprint arXiv:2309.13586},
year={2023}
}
If you have any questions, please feel free to contact Jiayi Chen at jiayichen@pku.edu.cn, and He Wang at hewang@pku.edu.cn.