# Improving Robotic Bin-Picking Performances through Human-Robot Collaboration
## 1 Introduction  
Since the introduction of robots to industrial processes, researchers have been increasingly interested in fully automating as many tasks as possible. One of them is the task of grasping objects randomly placed in a bin for subsequent manipulation or positioning in a different place and pose, and it is usually referred to as bin-picking. This task is challenging because it involves 3D-object recognition, grasping strategies, and path planning. In many industrial processes, such as assembly operations or kitting, most of the parts come in boxes or bins, where the parts are randomly located and potentially interlocked. As a regular practice, parts are unloaded either manually or automatically using part feeders, such as long conveyor belts. However, manual bin-picking has several drawbacks, such as the possibility of causing health problems due to the weight of parts and the typically limited task execution throughput [1]. Conversely, the use of part feeders is often expensive, is not flexible, and requires large spaces. For these reasons, many research groups have addressed the problem of enabling robots to perform bin-picking tasks, guided by machine vision and other sensors [2; 3; 4]. Full automation of bin-picking tasks is challenging because it involves 3D object recognition and pose estimation, grasp planning, path planning, and collision avoidance. Each of these problems represents a vast area of research in itself and is usually treated separately. Despite technical developments in all these areas, robotic arms still cannot surpass human hands in terms of speed, adaptability, and dexterity [5]. As a consequence, applications of robotic bin-picking are often difficult to implement and lack standardization. However, some general-purpose equipment is available on the market, which can be used just in basic bin-picking scenarios, especially for bins containing a single type of part with a relatively simple shape. On the contrary, handling mixed bins thatcontain multiple different types of parts with complex or unknown shapes remains an open and challenging task. Because humans and robots share complementary strengths in performing tasks, a collaborative bin-picking workcell where robots and humans work in close proximity can enhance bin-picking success rates, increase system flexibility and productivity while improving the operators' working conditions by exploiting the strengths of both humans and robots. Indeed, robots can repeatedly perform pick-and-place operations without fatigue, and humans excel in their perception and handling ability in unstructured environments. Human-robot collaboration is made possible by collaborative robots (cobots), which are designed to work uncaged and interact with operators in the same working environment. Despite current interest and advancements in collaborative robotics, few studies have addressed the collaboration between robots and human operators in bin-picking tasks. In [6; 7; 8], a remotely located human assists the robot in critical situations by solving any automated perception problems encountered during bin-picking. In [9], a collaborative robotic cell for bin-picking was proposed, where the interaction between the human and robot is limited to a laser sensor that determines how far the operator is from the robot, and the controller adjusts the robot's velocity accordingly. In [10], another collaborative bin-picking cell was proposed, where the robot can hand over the picked item to the operator or take it back. However, in previous studies, robot collaborative functions such as manual hand guidance or collision detection have not been exploited to solve typical bin-picking failures. This paper proposes a new framework for bin-picking, where a human operator working alongside the robot can help quickly and easily resolve faults when they inevitably occur.