GraspQP: Differentiable Optimization of Force Closure for Diverse and Robust Dexterous Grasping

Dexterous robotic hands enable versatile interactions through the flexibility and adaptability of a multi-finger setup, allowing for a wise range of task-specific grasp configurations in diverse environments. However, access to diverse and high-quality grasp data is essential to fully exploit the capabilities of dexterous hands, be it to train grasp prediction models from point clouds, train manipulation policies, or to support high-level task planning with a broader range of action options. Existing approaches for dataset generation rely on sampling-based algorithms or simplified force-closure analysis, which tend to converge to power grasps and often exhibit limited diversity. In this work, we propose a method to synthesize large-scale, diverse, and physically feasible grasps that additionally go beyond simple power grasps to more refined manipulation, such as pinches or tri-finger precision grasps. We introduce a rigorous differentiable energy formulation of force closure, implicitly defined through a Quadratic Program (QP). In addition, we present an adjusted optimization method (MALA*) that improves performance by dynamically rejecting gradient steps based on the global sample distribution. We extensively evaluate our approach and demonstrate significant improvements in both grasp diversity and the stability of final grasp predictions. Finally, we provide a new, large-scale grasp dataset for the 5’700 objects from DexGraspNet, consisting of five different grippers and three different grasp types.