个人信息
参与实验室科研项目
复杂环境下非完全信息博弈决策的智能基础模型研究
研究课题
人机共享控制中的非线性仲裁方法研究
学术成果
共撰写/参与撰写专利 0 项，录用/发表论文 2 篇，投出待录用论文2篇。
Journal Articles
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UNA-SAC: An Uncertainty-Aware Nonlinear Arbitration Method for Human-AI Shared Control
Shuyue Jiang,
Yun-Bo Zhao ,
Yu Kang,
Fei Xie,
and Yun-Sheng Zhao
IEEE Trans. Artif. Intell.
2026
[Abs]
[doi]
[pdf]
With the continuous development of artificial intelligence (AI), human-AI shared control has become an essential paradigm for achieving reliable collaboration, where the key challenge lies in efficiently arbitrating between human and AI policies. However, the inherent uncertainty of AI policies and their approximation errors often undermine the robustness and effectiveness of traditional linear arbitration. To address this issue, this paper proposes a nonlinear arbitration method based on the Soft Actor-Critic (SAC) framework, termed UNA-SAC. The method introduces a moment network to model AI policy uncertainty and incorporates a cognition-inspired mechanism to adjust the human policy, thereby constructing a distributional nonlinear arbitration form. Theoretical analysis demonstrates that the proposed method provides advantages in gradient optimization and effectively mitigates the cumulative effect of uncertainty-induced bias. Experimental results further validate its superiority in driving assistance scenarios: UNA-SAC achieves significant improvements in convergence speed, task success rate, robustness, and operational performance compared with linear arbitration and other baseline methods.
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A Dual Confidence Evaluation-Based Shared Control Approach for Human-Machine Collaboration
Yaqing Zhou,
Yun-Bo Zhao ,
Pengfei Li,
Xia Tian,
Shuyue Jiang,
and Yu Kang
Neurocomputing
2026
[Abs]
[pdf]
In recent years, shared control has received widespread attention for its potential to enhance humanmachine collaboration (HMC) performance. The core challenge lies in how to reasonably allocate control authority between humans and machines. However, existing rule-based or single-confidencebased allocation approaches often suffer from limited flexibility, high sensitivity to environmental changes, and complex mappings from decision signals, making them less effective in meeting the demands of dynamic tasks. To address these limitations, this paper proposes a Dual Confidence-based Shared Control (DC-SC) approach. First, human and machine decision confidence evaluation models are established to quantitatively describe the performance of both parties in the decision-making process. Then, a learnable shared control policy is designed based on these confidence evaluations, and the control authority is dynamically optimized in real time through environmental feedback within the policy gradient optimization framework, aiming to achieve optimal system performance in humanmachine collaboration. In addition, the key properties of the proposed confidence evaluation models are systematically analyzed from a theoretical perspective, further validating the rationality and reliability of the model design. Finally, experiments are conducted in both discrete and continuous action tasks. The results show that the proposed DC-SC approach outperforms both classical reinforcement learning-based machine control approach (A2C/DDPG) and a representative uncertainty-aware shared control approach (HULA) in terms of policy performance and system safety.
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