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Article
A stable data-augmented reinforcement learning method with ensemble exploration and exploitation
Learning from visual observations is a significant yet challenging problem in Reinforcement Learning (RL). Two respective problems, representation learning and task learning, need to solve to infer an optimal ...
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Article
Combination of the guide-weight criterion and BESO method for fast and stable topology optimization of two-dimensional continuum structures
This paper proposes a new method for topology optimization of two-dimensional (2D) continuum structures by combining the features of the guide-weight criterion and the conventional bidirectional evolutionary s...
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Article
A Graph-Based Deep Reinforcement Learning Approach to Gras** Fully Occluded Objects
Gras** in cluttered scenes is an important issue in robotic manipulation. The cooperation of gras** and pushing actions based on reinforcement learning is an effective means to obtain the target object whe...
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Chapter and Conference Paper
Repetitive Path Planning with Experience-Based Bidirectional RRT
Repetitive motion planning in semi-structured environments is involved in many robotics applications, such as manufacturing and in-orbit service of spatial robotic arms. In these scenarios, the environment is ...
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Article
Knitted self-powered sensing textiles for machine learning-assisted sitting posture monitoring and correction
With increasing work pressure in modern society, prolonged sedentary positions with poor sitting postures can cause physical and psychological problems, including obesity, muscular disorders, and myopia. In th...
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Article
Offline reinforcement learning with anderson acceleration for robotic tasks
Offline reinforcement learning (RL) can learn effective policy from a fixed batch of data without interaction. However, the real-world requirements, such as better performance and high sample efficiency, put s...
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Chapter and Conference Paper
Attention Guided 6D Object Pose Estimation with Multi-constraints Voting Network
For visual-based robotic manipulation, it has always been a challenging task to perform real-time and accurate pose estimation of target objects under cluttered background, illumination variations, occlusion, ...
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Chapter and Conference Paper
Sample-Efficient Reinforcement Learning Based on Dynamics Models via Meta-policy Optimization
Model-based reinforcement learning (RL) can acquire remarkable sample efficiency, which makes it a suitable choice for applications where experiment data is hard to collect. However, it is difficult to learn a...
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Article
SOAR Improved Artificial Neural Network for Multistep Decision-making Tasks
Recently, artificial neural networks (ANNs) have been applied to various robot-related research areas due to their powerful spatial feature abstraction and temporal information prediction abilities. Decision-m...
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Article
Fine semantic map** based on dense segmentation network
This paper proposes a fine semantic map** method using dense segmentation network (DS-Net) to obtain good performance of semantic map** fusion. First, the RGB image and the depth image are used to generate...
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Chapter and Conference Paper
Multi-robot Formation Control Using Reinforcement Learning Method
Formation is a good example of the research for multi-robot cooperation. Many different ways can be used to accomplish this task, but the main drawbacks of most of these methods are that robots can’t self-lear...
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Chapter and Conference Paper
Mandarin Voice Conversion Using Tone Codebook Map**
A tone codebook map** method is proposed to obtain a better performance in voice conversion of Mandarin speech than the conventional conversion method which deals mainly with short-time spectral envelopes. T...
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Chapter and Conference Paper
The Cognitive Behaviors of a Spiking-Neuron Based Classical Conditioning Model
A spiking-neuron based cognitive model with classical conditioning behaviors is proposed. With a reflex arc structure and a reinforcement learning method based on the Hebb rule, the cognitive model possesses t...