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 …
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 …
Learning-Aided Iterated Local Search Algorithm for Integrated Order Batching, Picker Assignment, Batch Sequencing, and Picker Routing Problem
ZC Cao, XS Lv, CR Lin - IEEE Transactions on Automation …, 2024 - ieeexplore.ieee.org
This work tackles an integrated order batching, picker assignment, batch sequencing, and
picker routing problem in warehouse environments. A Learning-Aided Iterated Local Search …
picker routing problem in warehouse environments. A Learning-Aided Iterated Local Search …
Large-margin Extreme Learning Machines with Hybrid Features for Wafer Map Defect Recognition
The critical information regarding the semiconductor manufacturing can be provided based
on the wafer map defect patterns. Automatic wafer map defect identification with machine …
on the wafer map defect patterns. Automatic wafer map defect identification with machine …
Vahagn: VisuAl Haptic Attention Gate Net for slip detection
J Wang, Y Ji, H Yang - Frontiers in Neurorobotics, 2024 - frontiersin.org
Introduction Slip detection is crucial for achieving stable grasping and subsequent
operational tasks. A grasp action is a continuous process that requires information from …
operational tasks. A grasp action is a continuous process that requires information from …
Probabilistic Spiking Neural Network for Robotic Tactile Continual Learning
The sense of touch is essential for robots to perform various daily tasks. Artificial Neural
Networks have shown significant promise in advancing robotic tactile learning. However …
Networks have shown significant promise in advancing robotic tactile learning. However …
Performing hardness classification using diffusive memristor based artificial neurons
Y Sharma, DP Pattnaik - Engineering Research Express, 2024 - iopscience.iop.org
Artificial neurons and synapses are the building blocks for constructing a neuromorphic
system such as Spiking Neural Network (SNN) or Artificial Neural Network (ANN). Recently …
system such as Spiking Neural Network (SNN) or Artificial Neural Network (ANN). Recently …
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 …
Evaluation of Continual Learning Methods for Object Hardness Recognition
How to solve the problem of continual learning of robotic tactile perception in an open,
dynamic environment is a pressing task. Although continual learning has been extensively …
dynamic environment is a pressing task. Although continual learning has been extensively …
Attention-enhanced BLSTM Network for Liquid Volume Estimation based on Tactile Sensing
The task of liquid volume estimation based on tactile sensing is challenging. Previous
researches mainly focused on the classification of different contents in the container, rather …
researches mainly focused on the classification of different contents in the container, rather …