# A Stackelberg Trust-Based Human-Robot Collaboration Framework for Warehouse Picking
## 2 Related Works
### Warehouse Picking  
Research on industrial picking operations has progressed through three main stages: manual picking, automated picking, and human-robot collaborative picking.  
1. Manual Picking  
Traditional picking operations rely on manual labor, where tasks are performed by workers manually moving goods or using simple equipment like forklifts and trailers. [PERSON] et al. demonstrated that the efficiency of manual picking is heavily influenced by warehouse layout and operations, conducting studies on warehouse design, storage allocation, order processing, and zoning [22]. [PERSON] et al. proposed a simulation method for designing efficient picking areas to improve productivity in manual picking systems [23]. [PERSON] et al. introduced a negotiation mechanism for real-time task allocation and developed new algorithms to optimize the picking process [24]. While these studies improved the efficiency and scalability of manual picking, the increasing picking volume driven by e-commerce, diverse delivery routes, and varying customer demands rendered manual picking costly and inefficient for modern logistics needs.  
1. Automated Picking  
In fully automated picking systems, all picking tasks are executed according to system-generated instructions. [PERSON] et al. conducted pioneering research on automated picking systems [25]. [PERSON] developed efficient algorithms to study the impact of product location allocation on expected throughput [26]. [PERSON] et al. examined storage allocation and order batching problems in Kiva mobile delivery systems [27]. Despite advancements, fully automated picking remains unachievable due to limitations in equipment size and capacity. Oversized and overweight items cannot be handled solely by robots, and manual work continues to play a vital role.  
To address the limitations of manual and automated picking, current research focuses on collaborative robots that work with humans to complete tasks in warehouse picking operations. [PERSON] et al. proposed a process model for collaborative picking environments that allows robots and humans to work together while enabling robots to improve their performance through human feedback [28]. [PERSON] et al. developed a human-robot collaborative system for industrial tasks aimed at optimizing picking operations [29]. [PERSON] et al. introduced a prototype of a novel manual picking assistance system and evaluated its effectiveness using three different approaches [30]. Compared to traditional manual and automated picking systems, human-robot collaboration combines their strengths. Manual picking has high labor costs, low efficiency, and slow response times, which are inadequate for the demands of e-commerce. Fully automated systems, on the other hand, are unsuitable for handling diverse, small-batch, and multi-order scenarios. Collaborative picking effectively addresses these challenges by integrating the advantages of both approaches.  
While human-robot collaborative picking has improved efficiency and performance, achieving higher levels of collaboration requires robots to predict human behavior. Since robots cannot directly capture human psychological states, it is necessary to construct an appropriate model to predict human behavior. This study adopts the concept of trust to achieve this goal.