Towards Grasping with Spiking Neural Networks for Anthropomorphic Robot Hands
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Representation and execution of movement in biology is an active field of research relevant to neurorobotics. Humans can remember grasp motions and modify them during execution based on the shape and the intended interaction with objects. We present a hierarchical spiking neural network with a biologically inspired architecture for representing different grasp motions. We demonstrate the ability of our network to learn from human demonstration using synaptic plasticity on two different exemplary grasp types (pinch and cylinder). We evaluate the performance of the network in simulation and on a real anthropomorphic robotic hand. The network exposes the ability of learning finger coordination and synergies between joints that can be used for grasping.
KeywordsGrasp motion representation Spiking networks Neurorobotics Motor primitives
The research leading to these results has received funding from the European Union Horizon 2020 Programme under grant agreement n.720270 (Human Brain Project SGA1).
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