Abstract
Synthesizing realistic articulated hand-object interactions is a fundamental problem in virtual reality, embodied intelligence, and digital human applications. Existing methods for dexterous grasp synthesis typically regress or denoise poses in a joint space that couples global rigid motion with local articulation, which often yields unstable samples and physically implausible contacts. We introduce DEAL-Grasp, built upon the Decoupled Alignment (DEAL) representation, which reformulates grasp synthesis as alignment-space generation: the interaction state comprises task-space geometric anchors and articulation parameters, from which the rigid transform is recovered via closed-form Procrustes alignment while preserving local articulation. On this mixed state, we model grasp generation using heterogeneous-state flow matching with component-wise vector fields, incorporating time-adaptive physical regularization during training. At inference, grasps are synthesized solely by integrating the learned vector field, without test-time optimization or auxiliary physical guidance. Across MultiDex and zero-shot RealDex benchmarks, DEAL-Grasp attains high force-perturbation success rates alongside minimal penetration and high diversity of generated grasps, while substantially reducing native inference latency compared to optimization-heavy baselines. The project page is available at https://wmtlab.github.io/DEAL-Grasp/.
Keywords
Subject
Publication details
- Journal
- Not available
- Open access
- Green open access
Cite this article
APA 7
Zhao, F., & Liu, Q. (2026). DEAL-Grasp: Decoupled Alignment Representation for Geometry-Aware Dexterous Grasp Generation. https://omanscience.com/en/articles/deal-grasp-decoupled-alignment-representation-for-geometry-aware-dexterous-grasp-generation
MLA 9
Zhao, Fuqiang, and Qian Liu. "DEAL-Grasp: Decoupled Alignment Representation for Geometry-Aware Dexterous Grasp Generation." https://omanscience.com/en/articles/deal-grasp-decoupled-alignment-representation-for-geometry-aware-dexterous-grasp-generation.
Chicago (author–date)
Zhao, Fuqiang, and Qian Liu. 2026. "DEAL-Grasp: Decoupled Alignment Representation for Geometry-Aware Dexterous Grasp Generation." https://omanscience.com/en/articles/deal-grasp-decoupled-alignment-representation-for-geometry-aware-dexterous-grasp-generation.
Harvard
Zhao, F. and Liu, Q. (2026) 'DEAL-Grasp: Decoupled Alignment Representation for Geometry-Aware Dexterous Grasp Generation', Available at: https://omanscience.com/en/articles/deal-grasp-decoupled-alignment-representation-for-geometry-aware-dexterous-grasp-generation.
Vancouver
Zhao F, Liu Q. DEAL-Grasp: Decoupled Alignment Representation for Geometry-Aware Dexterous Grasp Generation. https://omanscience.com/en/articles/deal-grasp-decoupled-alignment-representation-for-geometry-aware-dexterous-grasp-generation
IEEE
F. Zhao, and Q. Liu, "DEAL-Grasp: Decoupled Alignment Representation for Geometry-Aware Dexterous Grasp Generation," https://omanscience.com/en/articles/deal-grasp-decoupled-alignment-representation-for-geometry-aware-dexterous-grasp-generation.