# Trends of Human-Robot Collaboration in Industry Contexts: Handover, Learning, and Metrics
## 3 Current Advances and Trends
### Robot Learning  
It is a well-known fact that pre-programmed functions for a robot will narrow its capabilities. For environments where the purpose of the machine is to simply perform some repetitive tasks, this pre-programming strategy is more than suitable. However, when it comes to creating a collaborative robot, able to fluently execute dexterous tasks in coordination with a human operator, machine learning techniques development emerge as the best solution to achieve this goal. This kind of techniques can be implemented for proper recognition of all the scene that surrounds the robot (i.e., its human co-worker communication and its environment) and its respective classification. The correct classification of the surroundings allows the machine to easily predict what will probably happen in the near future, helping to anticipate the human motions, creating a more fluent task accomplishment. Moreover, the robot could also learn, through artificial intelligence methodologies, to move like a human, by transferring captions of human motion to the robot's space motion. By analysing sensory data from each human-robot collaborative system, it is possible to extract strong clues about the human co-worker. As an example, ref. [99] uses a laser range finder sensor to detect and track the human legs in order to recognize gait patterns. In [99], an adapted Hidden Markov Model (HMM) was developed to obtain an appropriate state estimation of human walk. Another example is presented in [100] where the HMM was used to estimate human affective state in real time by collecting data of heart rate, perspiration rate, and facial muscle contraction from several humans. For this type of classification, deep learning has proven to be the most recurrently used technique. The work present in [98] focuses on studying how deep representation learning should be used for human motion prediction and classification. In [101], a hand-eye coordination model for grasping was developed through deep learning and data collection. This study revealed the potential of this approach since novel objects, which were never considered during the training sessions, were successfully grasped by the robot. However, this type of procedure is widely known for requiring a huge amount of data to be considered effective. This concern appears as a shortcoming for using deep learning in other wider purposes. The work in [24] presents a deep learning framework for human motion capture data. This framework learns a generic representation from a extensive motion capture data andgeneralizes to new, unseen, motions. Once more, this type of approach has the shortcoming of requiring a large corpus of captured data in order to obtain acceptable results. Besides the human motion classification, one of the most relevant research within this topic of interest relies on human anticipation [96]. Predicting human movement highly impacts the user experience of collaborating with robots. This anticipation will give much more fluency to the performed task [97]. The capability of predicting human motion will promote a proactive system with high coordinated actions. To this end, several studies were already developed where machine learning is deeply discussed as the key tool for the optimal collaborative robot task performance [8; 93; 94; 95]. Apart from the perception and recognition of the environment, the robot must also plan its trajectory to achieve each proposed objective. For a collaborative robot, the action behavior should be synchronized with its human co-worker and fluent, similar to a human movement. This type of motion planning and control is hardly achieved through the classical analytical methodologies. For this reason, reinforcement learning has started to be implemented in HRC systems, aspiring to provide the tools for bringing the robots motion closer to human motion.