Y
Yingbai Hu
Researcher at Technische Universität München
Publications - 45
Citations - 1359
Yingbai Hu is an academic researcher from Technische Universität München. The author has contributed to research in topics: Robot & Mobile robot. The author has an hindex of 12, co-authored 45 publications receiving 602 citations. Previous affiliations of Yingbai Hu include Chinese Academy of Sciences & South China University of Technology.
Papers
More filters
Journal ArticleDOI
Improved recurrent neural network-based manipulator control with remote center of motion constraints: Experimental results.
TL;DR: An improved recurrent neural network (RNN) scheme is proposed to perform the trajectory control of redundant robot manipulators using remote center of motion (RCM) constraints to facilitate accurate task tracking based on the general quadratic performance index.
Journal ArticleDOI
Reinforcement Learning of Manipulation and Grasping Using Dynamical Movement Primitives for a Humanoidlike Mobile Manipulator
TL;DR: A reinforcement learning strategy for manipulation and grasping of a mobile manipulator is described, which reduces the complexity of the visual feedback and handle varying manipulation dynamics and uncertain external perturbations.
Journal ArticleDOI
Development of Sensory-Motor Fusion-Based Manipulation and Grasping Control for a Robotic Hand-Eye System
TL;DR: A series of manipulation tasks consisting of tracking/recogniting/grasping of an object are implemented, and experiment results exhibit the responsiveness and flexibility of the proposed sensory motion fusion approach.
Journal ArticleDOI
Fuzzy-Torque Approximation-Enhanced Sliding Mode Control for Lateral Stability of Mobile Robot
TL;DR: A flexible lateral control scheme is considered for the developed wheel-legged robot, which consists of a cubature Kalman algorithm to evaluate the centroid slip angle and the yaw rate and a fuzzy compensation and preview angle-enhanced sliding model controller to improve the tracking accuracy and robustness.
Journal ArticleDOI
An Incremental Learning Framework for Human-like Redundancy Optimization of Anthropomorphic Manipulators
TL;DR: The swivel motion reconstruction approach was applied to imitate human-like behavior using the kinematic mapping in robot redundancy and showed that the architecture could not only enhance the regression accuracy but also significantly reduce the processing time of learning human motion data.