# Trends of Human-Robot Collaboration in Industry Contexts: Handover, Learning, and Metrics
## 5 Conclusions  
Innumerable tasks can only be performed by humans (instead of robots) due to the high degree of complexity, dexterous coordination, perception, interpretation and decision capabilities they require. Even in an industrial environment, where the presence of automated robots is already a well established reality, several objectives can only be achieved through the collaborative work of human teams. In the past two decades, collaborative robots have been increasingly studied, developed and integrated in various sectors of the industry. These collaborative robots are not completely agnostic to the surroundings of their operational space, being capable of adapting their movements to possible human interventions during the task performance. This adaptation has several advantages such as safety promotion and human guiding acceptance. This study was composed of two main parts. The first one reviewed the evolution of Human-Robot Interaction (HRI) and Human-Robot Collaboration (HRC) conceptualizations, enumerating various interfaces through which humans are able to interact with robots. Moreover, and considering that physical contact stands as the ultimate form of interaction, physicality was also analyzed. All the referenced works regarding these topics symbolize the extensive developments of human-robot interaction. Every interplay channel, such as gesture, human motion, voice, haptics, EMG/EEG, GUI, and the perception of physical contact between the robot and an object, or a human, should be considered for future advancements in Human-Robot Collaboration, since every one of these subjects contains a reliable base for communication, safety, and grasping. The second part of this study focused on the current trends for Human-Robot Collaboration, discussing the possible future challenges that this open field still holds on. The reality of industrial use cases can only be effectively covered up by exploring the conception of an adaptable handover controller that accommodates a wide range of situations. This exploration requires addressing several issues that can be synthesized as follows. First, we followed the premise that the more similar to human-human transfer of objects, the better will be the human-robot objects handover process. Nonetheless, there are several open questions to ensure a completely fluent and general object handover process between robots and humans assuming the behavior of the \"giver\" or the \"receiver\". Some unresolved challenges are related to the general possible hands pose, object orientation, object shape and to the object possession decision. Second, the integration of cognitive abilities (e.g., interpretation, prediction, and learning) with perception and interaction abilities promise to create robots able to interact meaningfully with their human counterparts. Despite the implementation of distinct learning methods (deep learning and reinforcement learning), using several models (HMM, GMM, DMP), in collaborative robots, additional efforts are still required to overcome some concerns to handle with the real world high variability of industrial settings: modeling and interpreting world's reality, dividing complex tasks in simpler checkpoints and learning to learn. Third, to perform a systematic study of current approaches to tackle the challenges of object handovers and their current limitations, it is important to understand how to evaluate the different solutions and if there is a lack of appropriate metrics. Bearing all this in mind, the goal of near future Human-Robot Collaborations is to aggregate both cognitive knowledge (interpretation, prediction, motion planning and learning) and physical knowledge (grasping, object release, human contact) concerning the robot, making it skillful and qualified to help humans in collaborative tasks. Hopefully, future technological evolution in this domain will assist human life in a fruitful and healthy way. Conceptualization, A.C., F.S. and V.S. ; investigation, A.C. and F.S. ; writing--original draft preparation, A.C. and F.S.