Hao Zhu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Hao Zhu has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Jialin Wang
State Key Laboratory of ASIC and Systems, The Institute of Brain-Inspired Circuits and Systems, Fudan University, Shanghai 201203, China
3 shared publications - Richard Chuan-Jin Shi
Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
2 shared publications - Rui Gao
MOE Key Laboratory of Marine Intelligent Equipment and System, State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
2 shared publications Haotian Zheng
State Key Laboratory of ASIC and Systems, the Institute of Brain-Inspired Circuits and Systems, and the Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China
1 shared publication- Jialin Wang
Fudan University, Shanghai, China
1 shared publication - Kefan Jin
Shanghai Jiao Tong University
1 shared publication
Latest Publications
- Open Access
- 30 May 2025
- Published by MDPI AG in Applied Sciences
- Vol. 15 (11) , 6163
EEG-Based Inverse Reinforcement Learning for Safety-Oriented Global Path Planning in Dynamic Environments
Recent advancements in lightweight electroencephalogram(EEG) signal classification have enabled real-time human–robot interaction, yet challenges persist in balancing computational efficiency and safety in dynamic path planning. This study proposes an EEG-based inverse reinforcement learning (EIRL) framework to simulate human navigation strategies by decoding neural decision preferences. The method integrates a pruned WNFG-SSCCNet-ADMM classifier for EEG signal mapping, apprenticeship learning for reward function extraction, and Q-learning for policy optimization. Experimental validation in an 8 × 8 FrozenLake-v1 environment demonstrates that EIRL reduces average path risk values by 50% compared with traditional reinforcement learning, achieving expert-level safety ( = 4) while maintaining optimal path lengths. The framework enhances adaptability in unknown environments by embedding human-like risk aversion into robotic planning, offering a robust solution for applications requiring minimal prior environmental knowledge. Results highlight the synergy between neural feedback and computational models, advancing inclusive human–robot collaboration in safety-critical scenarios.- 30 May 2025
- Published by MDPI AG in Applied Sciences
- Vol. 15 (11) , 6163
Open AccessEEG-Based Inverse Reinforcement Learning for Safety-Oriented Global Path Planning in Dynamic Environments
Recent advancements in lightweight electroencephalogram(EEG) signal classification have enabled real-time human–robot interaction, yet challenges persist in balancing computational efficiency and safety in dynamic path planning. This study proposes an EEG-based inverse reinforcement learning (EIRL) framework to simulate human navigation strategies by decoding neural decision preferences. The method integrates a pruned WNFG-SSCCNet-ADMM classifier for EEG signal mapping, apprenticeship learning for reward function extraction, and Q-learning for policy optimization. Experimental validation in an 8 × 8 FrozenLake-v1 environment demonstrates that EIRL reduces average path risk values by 50% compared with traditional reinforcement learning, achieving expert-level safety ( = 4) while maintaining optimal path lengths. The framework enhances adaptability in unknown environments by embedding human-like risk aversion into robotic planning, offering a robust solution for applications requiring minimal prior environmental knowledge. Results highlight the synergy between neural feedback and computational models, advancing inclusive human–robot collaboration in safety-critical scenarios. - Open Access
Timed-Elastic-Band-Based Variable Splitting for Autonomous Trajectory Planning
Existing trajectory planning methods often face challenges in ensuring stable robot motion control, leading to significant positional errors during navigation. This study proposes Timed-Elastic-Band-Based Variable Splitting (TEB-VS), a novel framework that integrates variable splitting (VS)—a constrained optimization technique—with the classical Timed-Elastic-Band (TEB) algorithm. Unlike incremental modifications to TEB, TEB-VS introduces a systematic combination of VS and TEB to decompose non-convex global constraints into tractable subproblems while leveraging symmetry principles for balanced multi-objective control (e.g., velocity, acceleration, and obstacle avoidance). Experimental results demonstrate that TEB-VS achieves a improvement in motion stability over traditional TEB in obstacle-free simulations and a enhancement in dynamic obstacle scenarios. Real-world tests show a reduction in angular velocity oscillations, with computational efficiency comparable to TEB. The framework’s effectiveness in harmonizing trajectory smoothness and dynamic adaptability is validated through extensive simulations and TurtleBot2 experiments.Open AccessTimed-Elastic-Band-Based Variable Splitting for Autonomous Trajectory Planning
Existing trajectory planning methods often face challenges in ensuring stable robot motion control, leading to significant positional errors during navigation. This study proposes Timed-Elastic-Band-Based Variable Splitting (TEB-VS), a novel framework that integrates variable splitting (VS)—a constrained optimization technique—with the classical Timed-Elastic-Band (TEB) algorithm. Unlike incremental modifications to TEB, TEB-VS introduces a systematic combination of VS and TEB to decompose non-convex global constraints into tractable subproblems while leveraging symmetry principles for balanced multi-objective control (e.g., velocity, acceleration, and obstacle avoidance). Experimental results demonstrate that TEB-VS achieves a improvement in motion stability over traditional TEB in obstacle-free simulations and a enhancement in dynamic obstacle scenarios. Real-world tests show a reduction in angular velocity oscillations, with computational efficiency comparable to TEB. The framework’s effectiveness in harmonizing trajectory smoothness and dynamic adaptability is validated through extensive simulations and TurtleBot2 experiments. - Open Access
Timed-Elastic-Band Based Variable Splitting for Autonomous Trajectory Planning
Existing trajectory planning methods are struggling to handle the issue of autonomous track swinging during navigation, resulting in significant errors when reaching the destination. In this article, we address autonomous trajectory planning problems, which aims at developing innovative solutions to enhance the adaptability and robustness of unmanned systems in navigating complex and dynamic environments. We first introduce the variable splitting (VS) method as a constrained optimization method to reimagine the renowned Timed-Elastic-Band (TEB) algorithm, resulting in a novel collision avoidance approach named Timed-Elastic-Band based variable splitting (TEB-VS). The proposed TEB-VS demonstrates superior navigation stability, while maintaining nearly identical resource consumption to TEB. We then analyze the convergence of the proposed TEB-VS method. To evaluate the effectiveness and efficiency of TEB-VS, extensive experiments have been conducted using TurtleBot2 in both simulated environments and real-world datasets.
Open AccessTimed-Elastic-Band Based Variable Splitting for Autonomous Trajectory Planning
Existing trajectory planning methods are struggling to handle the issue of autonomous track swinging during navigation, resulting in significant errors when reaching the destination. In this article, we address autonomous trajectory planning problems, which aims at developing innovative solutions to enhance the adaptability and robustness of unmanned systems in navigating complex and dynamic environments. We first introduce the variable splitting (VS) method as a constrained optimization method to reimagine the renowned Timed-Elastic-Band (TEB) algorithm, resulting in a novel collision avoidance approach named Timed-Elastic-Band based variable splitting (TEB-VS). The proposed TEB-VS demonstrates superior navigation stability, while maintaining nearly identical resource consumption to TEB. We then analyze the convergence of the proposed TEB-VS method. To evaluate the effectiveness and efficiency of TEB-VS, extensive experiments have been conducted using TurtleBot2 in both simulated environments and real-world datasets.
- Open Access
- 16 March 2023
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks and Learning Systems
- Vol. 35 (9) , 12157-12171
SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification
In this article, we propose a sparse spectra graph convolutional network (SSGCNet) for epileptic electroencephalogram (EEG) signal classification. The goal is to develop a lightweighted deep learning model while retaining a high level of classification accuracy. To do so, we propose a weighted neighborhood field graph (WNFG) to represent EEG signals. The WNFG reduces redundant edges between graph nodes and has lower graph generation time and memory usage than the baseline solution. The sequential graph convolutional network is further developed from a WNFG by combining sparse weight pruning and the alternating direction method of multipliers (ADMM). Compared with the state-of-the-art method, our method has the same classification accuracy on the Bonn public dataset and the spikes and slow waves (SSW) clinical real dataset when the connection rate is ten times smaller.
- 16 March 2023
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks and Learning Systems
- Vol. 35 (9) , 12157-12171
Open AccessSSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification
In this article, we propose a sparse spectra graph convolutional network (SSGCNet) for epileptic electroencephalogram (EEG) signal classification. The goal is to develop a lightweighted deep learning model while retaining a high level of classification accuracy. To do so, we propose a weighted neighborhood field graph (WNFG) to represent EEG signals. The WNFG reduces redundant edges between graph nodes and has lower graph generation time and memory usage than the baseline solution. The sequential graph convolutional network is further developed from a WNFG by combining sparse weight pruning and the alternating direction method of multipliers (ADMM). Compared with the state-of-the-art method, our method has the same classification accuracy on the Bonn public dataset and the spikes and slow waves (SSW) clinical real dataset when the connection rate is ten times smaller.