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Implicit representation priors meet Riemannian geometry for Bayesian robotic grasping
Robotic grasping in highly noisy environments presents complex challenges, especially with limited prior knowledge about the scene.
Simulation-based Bayesian inference for robotic grasping
General robotic grippers are challenging to control because of their rich nonsmooth contact dynamics and the many sources of uncertainties due to the environment or sensor noise.
Simulation-based Bayesian inference for multi-fingered robotic grasping
Multi-fingered robotic grasping is an undeniable stepping stone to universal picking and dexterous manipulation.
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