# Trends of Human-Robot Collaboration in Industry Contexts: Handover, Learning, and Metrics
## 2 Human-Robot Collaboration Concepts
### Interfaces of Communication
For this reason, their research dives into this particular interface of communication, enumerating three types of gestures: body gestures (full body actions or motions); hand and arm gestures (arm poses, hand gestures), and head and facial gestures (nodding or shaking head, winking lips). Additionally, ref. [14] proposes a workflow of the steps for communication interpretation by the robot. First, robot sensors have to capture the gesture raw data (_sensor data collection_); secondly, a gesture must be identified within all the data for each frame (_gesture identification_); thirdly, the identified gesture is tracked during the movement (_gesture tracking_); fourthly, according to pre-defined gestures type, the tracked gesture movement is classified (_gesture classification_); lastly, the robot translates this gesture recognition into  
Figure 3: Overview of communication interfaces for HRC and some associate base sensors. robot commands (_gesture mapping_). All these steps seem very explicit an understandable, and can be, with small adjustments, extrapolated for all types of communication interfaces. Another interface of communication is established by the combination of microphones and voice recognition algorithms. This type of communication is possibly the most similar to the usual communication between humans. However, an important issue regarding voice communication is recognized in [23]: in an industrial environment, the presence of background noise and chatter can make it problematic to perceive speech commands. From the same concern about the industrial adverse environmental conditions for a good performance of vision-based and/or voice-based interaction systems, ref. [16] presents an approach that relies on the speech and gesture recognition, dealing with information that can potentially be contradictory or complementary. Apart from these two particular topics, a distinct channel of communication between humans and robots is also discussed in [8]: electromyography (EMG) and electroencephalography (EEG) sensors. When a muscle contracts, electrical signals are generated and measurable by an EMG sensor. EEG signals are the most used ones for brain-computer interfaces (BCIs). In [4], in addition to all the mentioned communication channels, graphical user interfaces (GUI) are also included as a type of interaction between the human and the robot. The author states that an operator can control a collaborative robot through a communication interface that can be verbal (speech) or nonverbal. Nonverbal communication includes gestures, gaze, head pose, haptics and UIs. Similarly to the previous study, ref. [4] provides, in a much more summarized way, the general workflow that the collaborative robot must follow to communicate: signals are detected, recognised and mapped to executable actions for the robot. Notice that, by using a GUI, there is no need for sensors or for data recognition and classification algorithms, since the provided information is directly received by the robot, in a \"language\" that it knows since it was built. A new type of interaction is also described in [8]: physical Human-Robot Collaboration (pHRC). In this type of collaboration, it becomes imperative for the robot to be able to observe its surroundings in order to take cognizance and continuous update of the current state. Based on that information, the robot could be endowed with the ability to estimate desired actions to be performed and the best possible way to perform them. In the example presented in [8], the human and the robot are performing a table-lifting task. For the successful completion of the task, the collaborative robot should identify the items in the environment (such as the table) and predict human future intention in order to act accordingly. Robotic skin tactile sensors (haptics) is also addressed in [8]. Robotic systems could be capable of identifying objects via multiple contact points thanks to the direct feedback that this technology provides.