Learning from Electromyography Synergies to Grasp Novel Objects by Superquadric Representation


The objective is to learn grasp synergies with high fidelity given object pose and geometry. However, under-actuated, anthropomorphic hands require complex, high dimensional control strategies. Including object pose and geometry further increase the size of the state space. Therefore, grasping in unstructured environments in the same fashion as humans proves to be non-trivial.