周雅情 田霞 蒋舒悦 李鹏飞 题为 “A Dual Confidence Evaluation-Based Shared Control Approach for Human-Machine Collaboration” 的论文已被《Neurocomputing》接受发表。该论文摘要如下：

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.