Using differentiable Gaussian mixtures to represent grasp uncertainty enables fast, gradient-based optimization for worst-case robustness—achieving 10x speedup over particle filters while maintaining or improving success rates.
This paper tackles the problem of robust robotic grasping when contact forces, sensing, and external disturbances are unpredictable. Instead of using slow particle-filter approaches, the authors represent uncertainty as a learnable Gaussian mixture and optimize for worst-case performance (CVaR) using gradient-based methods.