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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 dynamical system approach that couples task and joint space by means of an attractor-based content addressable memory. The respective recurrent reservoir network simultaneously provides a novel control framework for goal directed movement generation. The network first learns to associate end effector coordinates with joint angles by means of reservoir attractor states and thereby implements...
This paper presents a rehabilitation robot used for the patient with paralysis of lower limb and the kinematics of the mechanism is analyzed. The mechanical design of the robot is described. The forward and inverse kinematics solution of the robot is given. The working space of the foot apex is calculated under the training range. The trajectory planning is studied. It provides important data reference...
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 objective of practical training is a major issue in students education, in many engineering disciplines. The access to specialized technological equipment for education is often limited by specific time restriction, or not provided at all. Therefore, the benefits by providing a Web-based platform for remote experimentation via LAN or Internet are evident. This paper describes the development of...
Rehabilitation robots start to become an important tool in stroke rehabilitation. Compared to manual arm training, robot-supported training can be more intensive, of longer duration, repetitive and task-oriented. Therefore, these devices have the potential to improve the rehabilitation process in stroke patients. While in the past, most groups have been working with endeffector-based robots, exoskeleton...
In this paper, the Bees algorithm was used to train multi-layer perceptron neural networks to model the inverse kinematics of an articulated robot manipulator arm. The Bees Algorithm is a recently developed parameter optimisation algorithm that is inspired by the foraging behaviour of honey bees. The Bees Algorithm performs a kind of exploitative neighbourhood search combined with random explorative...
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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