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We show that inverse kinematics of different tools can be efficiently learned with a single recurrent neural network. Our model exploits all upper body degrees of freedom of the Honda's humanoid robot research platform. Both hands are controlled at the same time with parametrized tool geometry. We show that generalization both in space as well as across tools is possible from very few training data...
We present a neural network approach to early motor learning. The goal is to explore the needs for boot-strapping the control of hand movements in a biologically plausible learning scenario. The model is applied to the control of hand postures of the humanoid robot ASIMO by means of full upper body movements. For training, we use an efficient online scheme for recurrent reservoir networks consisting...
The future of robotics is not limited to factories and homes, and is extending to robot-assisted urban search and rescue. This paper proposes a new application of neural networks in this emerging field of research. Specifically, a neural network with feedforward architecture using the backpropagation learning algorithm is implemented in order to determine the positioning of a robotic gripper that...
A fast and efficient method for computing optimal grasping and manipulation forces is presented based on a Quadratic Optimisation formulation for a hand robotics system, where computation has been based on using the non-linear factual model of contacts. Furthermore, in order to achieve grasping while in motion, the Hand Inverse Jacobian has to be intensively computed, consequently, we investigate...
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