Topology Aware Data-Driven Inverse Kinematics

Attractor-based computation with reservoirs for online learning of inverse kinematics

We implement completely data driven and efficient online learning from temporally correlated data in a reservoir network setup. We show that attractor states rather than transients are used for computation when learning inverse kinematics for the redundant robot arm PA-10. Our findings shade also light on the role of output feedback.

Control of Robotic Manipulators using G . A . & A .

This paper presents application of G.A. to find out the topology of different A.N.N. techniques for the control of Robot. The neural network is trained for the inverse kinematics by providing it with a random data set of the manipulator spread over its workspace. Neural Networks have been used due to the inherent limitations of the conventional techniques to solve the inverse kinematics problem...

TAC: A Topology-Aware Chord-based Peer-to-Peer Network

Among structured Peer-to-Peer systems, Chord has a general popularity due to its salient features like simplicity, high scalability, small path length with respect to network size, and flexibility on node join and departure. However, Chord doesn’t take into account the topology of underlying physical network when a new node is being added to the system, thus resulting in high routing late...

Hybrid Motion Control combining Inverse Kinematics and Inverse Dynamics Controllers for Simulating Percussion Gestures

Virtual characters playing virtual musical instruments in a realistic way need to interact in real-time with the simulated sounding environment. Dynamic simulation is a promising approach to finely represent and modulate this interaction. Moreover, capturing human motion provides a database covering a large variety of gestures with different levels of expressivity. We propose in this paper a ne...

GMDH Type Neural Networks and Their Application to the Identification of the Inverse Kinematics Equations of Robotic Manipulators (RESEARCH NOTE)