A shortcut enhanced LSTM-GCN network for multi-sensor based human motion tracking
Multi-sensor based motion tracking is of great interest to the robotics community as it may
lessen the need for expensive optical motion capture equipment. However, the traditional …
lessen the need for expensive optical motion capture equipment. However, the traditional …
An RNN-LSTM enhanced compact and affordable micro force sensing system for interventional continuum robots with interchangeable end-effector instruments
Micro force sensing in various clinical scenarios is a challenging issue to be addressed. It is
highly difficult to trade off the size, cost, and measurement accuracy of a micro force sensing …
highly difficult to trade off the size, cost, and measurement accuracy of a micro force sensing …
Tactonet: Tactile ordinal network based on unimodal probability for object hardness classification
Hardness is one of the most critical tactile properties for robots to recognize objects.
Machine learning methods have shown superior performance in object hardness …
Machine learning methods have shown superior performance in object hardness …
A deep learning method based on triplet network using self-attention for tactile grasp outcomes prediction
Recent research work has demonstrated that pregrasp tactile information can be used to
effectively predict whether a grasp will be successful or not. However, most of the existing …
effectively predict whether a grasp will be successful or not. However, most of the existing …
Iterative minority oversampling and its ensemble for ordinal imbalanced datasets
N Wang, ZL Zhang, XG Luo - Engineering Applications of Artificial …, 2024 - Elsevier
Ordinal classification of imbalanced datasets is a challenging problem that occurs in many
real-world applications. The main challenge is to simultaneously consider the classes …
real-world applications. The main challenge is to simultaneously consider the classes …
A dictionary-based approach to time series ordinal classification
Abstract Time Series Classification (TSC) is an extensively researched field from which a
broad range of real-world problems can be addressed obtaining excellent results. One sort …
broad range of real-world problems can be addressed obtaining excellent results. One sort …
Feature Distance Representer: Exploring the Connection between Point Cloud Feature Levels from the Perspective of Distance
K You, Z Hou, J Liang, H Liu - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
With the advent of the Industry 4.0 era, intelligent driving, medical impact, industrial
automation, and other fields have developed rapidly, point cloud acquisition technology and …
automation, and other fields have developed rapidly, point cloud acquisition technology and …
COMPlacent: A Compliant Whisker Manipulator for Object Tactile Exploration
Handling fragile objects requires minimally invasive interaction skills in order to avoid any
permanent deformation, alternation or damages. Such need is often required in tactile …
permanent deformation, alternation or damages. Such need is often required in tactile …
Design and Psychophysical Evaluation of a Novel Wearable Upper-Arm Tactile Display Device
Y Zhu, PX Liu, J Gao - Sensors, 2023 - mdpi.com
A novel wearable upper arm tactile display device, which can simultaneously provide three
types of tactile stimuli (ie, squeezing, stretching, and vibration) is presented. The squeezing …
types of tactile stimuli (ie, squeezing, stretching, and vibration) is presented. The squeezing …
A Self-supervised Contrastive Learning Method for Grasp Outcomes Prediction
In this paper, we probe the proficiency of contrastive learning techniques in forecast grasp
outcomes, without supervision. Employing a dataset that's open to the public, we establish …
outcomes, without supervision. Employing a dataset that's open to the public, we establish …