Autoencoder-based transfer learning in brain–computer interface for rehabilitation robot

Intelligent User Interface for a Rehabilitation Robot

Inter-robot transfer learning for perceptual classification

We introduce the novel problem of inter-robot transfer learning for perceptual classification of objects, where multiple heterogeneous robots communicate and transfer learned object models consisting of a fusion of multiple object properties. Unlike traditional transfer learning, there can be severe differences in the data distributions, resulting from differences in sensing, sensory processing...

Human arm kinematics for robot based rehabilitation

The paper considers a technique for computation of the inverse kinematic model of the human arm for robot based rehabilitation that uses measurements of the hand position and orientation and radial acceleration of the upper arm. Analytical analysis and empirical validation of the method are presented. The algorithm enables estimation of human arm angles, which can be used in trajectory planning...

Variation Autoencoder Based Network Representation Learning for Classification

Network representation is the basis of many applications and of extensive interest in various fields, such as information retrieval, social network analysis, and recommendation systems. Most previous methods for network representation only consider the incomplete aspects of a problem, including link structure, node information, and partial integration. The present study introduces a deep networ...

Transfer Learning for Reinforcement Learning on a Physical Robot

As robots become more widely available, many capabilities that were once only practical to develop and test in simulation are becoming feasible on real, physically grounded, robots. This newfound feasibility is important because simulators rarely represent the world with sufficient fidelity that developed behaviors will work as desired in the real world. However, development and testing on robo...