Bio-inspired Control of Dexterous Manipulation
Robots successfully manipulate objects in controlled environments. However, they fail in unknown environments. Few years old children lift and manipulate unfamiliar objects more dexterously than today’s robots. Therefore, roboticists are looking for inspiration on neurophysiological studies to improve their robotics control models. We present an artificial intelligence control model for dexterous manipulation, and a grip and load force control algorithm, strongly inspired on neurophysiological studies of the human manipulation process.
KeywordsRobotics dexterous manipulation neural networks reinforcement learning
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