Guo Yujie, Yi Pengfei, Wei Xiaopeng, Zhou Dongsheng
Key Laboratory of Advanced Design and Intelligent Computing (Ministry of Education), School of Software Engineering, Dalian University, Dalian, 116622, China.
School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China.
Vis Comput Ind Biomed Art. 2025 Jun 23;8(1):16. doi: 10.1186/s42492-025-00198-7.
In human-robot collaborative tasks, human trust in robots can reduce resistance to them, thereby increasing the success rate of task execution. However, most existing studies have focused on improving the success rate of human-robot collaboration (HRC) rather than on enhancing collaboration efficiency. To improve the overall collaboration efficiency while maintaining a high success rate, this study proposes an active interaction strategy generation for HRC based on trust. First, a trust-based optimal robot strategy generation method was proposed to generate the robot's optimal strategy in a HRC. This method employs a tree to model the HRC process under different robot strategies and calculates the optimal strategy based on the modeling results for the robot to execute. Second, the robot's performance was evaluated to calculate human's trust in a robot. A robot performance evaluation method based on a visual language model was also proposed. The evaluation results were input into the trust model to compute human's current trust. Finally, each time an object operation was completed, the robot's performance evaluation and optimal strategy generation methods worked together to automatically generate the optimal strategy of the robot for the next step until the entire collaborative task was completed. The experimental results demonstrates that this method significantly improve collaborative efficiency, achieving a high success rate in HRC.
在人机协作任务中，人类对机器人的信任可以减少对它们的抵触情绪，从而提高任务执行的成功率。然而，大多数现有研究都集中在提高人机协作（HRC）的成功率上，而不是提高协作效率。为了在保持高成功率的同时提高整体协作效率，本研究提出了一种基于信任的人机协作主动交互策略生成方法。首先，提出了一种基于信任的最优机器人策略生成方法，以在人机协作中生成机器人的最优策略。该方法采用一棵树对不同机器人策略下的人机协作过程进行建模，并根据建模结果计算出机器人执行的最优策略。其次，对机器人的性能进行评估，以计算人类对机器人的信任度。还提出了一种基于视觉语言模型的机器人性能评估方法。评估结果被输入到信任模型中，以计算人类当前的信任度。最后，每次完成对象操作时，机器人的性能评估和最优策略生成方法共同作用，自动生成机器人下一步的最优策略，直到整个协作任务完成。实验结果表明，该方法显著提高了协作效率，在人机协作中取得了较高的成功率。
