# A Stackelberg Trust-Based Human-Robot Collaboration Framework for Warehouse Picking
## 6 Conclusions and Future Work  
This paper proposes a Stackelberg trust strategy-based human-robot collaboration framework for warehouse picking. By constructing a POSG model, the collaborative operation scenario is modeled, and human trust is represented as a hidden state in a partially observable form. A Bayesian posterior-based state space is employed for the real-time evaluation of human trust by robots. This framework further considers the impact of human factors in the work environment on human-robot collaboration, specifically incorporating the human fatigue function into the collaboration efficiency reward function. Based on trust dynamics, human strategies are generated, and the Stackelberg strategy is utilized to solve the robot's strategy. To account for the effects of continuous work in industrial environments, the Bellman equation is applied to achieve multi-round Stackelberg strategy solutions, referred to as the ISTSG algorithm. A POMDP model was constructed in PRISM to represent the derived strategies, with no-trust and single-round trust strategy models set up as baselines for comparison. Experiments were conducted to validate the proposed framework. The experimental results demonstrate that the proposed framework improves the efficiency of human-robot collaboration and significantly reduces human fatigue within collaborative tasks.  
This work primarily focuses on the warehouse picking environment, but the proposed framework can be extended to various scenarios in the future. Moreover, this framework only considers one-to-one human-robot collaboration, while multi-robot scenarios are a future trend. Consequently, one-to-many human-robot collaborations will be a key focus for future research. Future work will explore two key extensions. First, we will analyze additional environmental factors like robot path selection impacts. Second, we plan to implement multiple reinforcement learning algorithms as comparative benchmarks.  
Conceptualization, Y.L. and F.G.; methodology, Y.L. and Y.M.; software, F.G.; validation, Y.L., F.G., and Y.M.; formal analysis, Y.L.; investigation, Y.L.; resources, F.G.; data curation, F.G.; writing--original draft preparation, Y.L. and F.G.; writing--review and editing, Y.L.; visualization, F.G.; supervision, Y.L.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The National Social Science Fund of China under Grant No. 24 BGL111.  
The data presented in this study are available on request from the corresponding author.  
We thank all those who contributed to this article.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **(0.9, 0.1)** & **(0.7, 0.3)** & **(0.5, 0.5)** & **(0.3, 0.7)** & **(0.1, 0.9)** & **Mean** \\ \hline Multi-round vs. No-trust & 0.23\% & \(-1.52\%\) & \(-3.04\%\) & \(-4.36\%\) & \(-5.18\%\) & \(-2.77\%\) \\ \hline Multi-round vs. Single-round & \(-0.09\%\) & \(-0.12\%\) & \(-0.10\%\) & \(-0.11\%\) & \(0.09\%\) & \(-0.06\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model accuracy comparison.