# Trends of Human-Robot Collaboration in Industry Contexts: Handover, Learning, and Metrics
## 3 Current Advances and Trends
### Metrics in HRC  
Another current interest in Human-Robot Collaboration is how to measure the effectiveness, efficiency, fluency and adaptability of one these HRC systems. The definition of adequate metrics and guidelines to evaluate human-robot interaction tasks' performance is, in [3], recognized as a truly important aspect to lead future developments. Several research studies have focused on the definition for metrics for Human-Robot Interaction [107; 108; 109; 110]. In [107], the metrics related to HRI are subdivided into several particular domains such as productivity, efficiency, reliability, safety, and co-activity. This metrics categorization is easily understandable through the natural complexity that every Human-Robot Collaboration procedure entails. It is difficult to assess such systems in a global and objective evaluation. In fact, many of the studied metrics in [107] cannot be generalized to an open mechanism for measuring the feature of its category. [PERSON] et al. [110] discuss four specific metrics for evaluating human-robot fluency: the percentage of concurrent activity, the human's idle time, the robot's functional delay and the robot's idle time. These metrics are general and agnostic to the specific content of the collaborative acts, relating only to periods of activity. Anyhow, the metrics were used to evaluate an objects operation task in a human-robot shared workspace. More specifically, regarding the human-robot object handover domain, the survey from [112] reports a comprehensive list of qualitative and quantitative metrics and identifies the major areas of improvement to reach performance comparable to human interaction. However, none of these metrics seems to cover completely every human criteria about the fluency and efficiency of a collaborative task performance. For example, the metrics presented in [110] do not fit every collaborative activities, since many simplifying assumptions were made such as the assumption that the time of the whole task is the same for the human and for the robot. Besides this, the authors of [110] declare that the presented metrics does not cover some aspects of physically coupled collaboration. Another interesting reported issue in [112] is that the large majority of human-robot object handover studies use only a single object class for the experiments: most of this objects were bottles or boxes. For a potential work with the purpose of generalizing the handover procedure to any possible object, a metric to separate different object in classes (through some descriptors), and to measure the success rate for each object class, is required. The chosen metrics for human-robot object handover procedures should consider numerous factors, and several works have already created and used some measurement tools to analyse quantitatively and qualitatively the task of transferring objects between robots and humans. In [108], the evaluation of the object handover procedure is built upon a simple success/failure rate by measuring how many times the object was delivered within the total times of trial. This is an example of a basic quantitative metric, since the quality of the interaction was not evaluated. For the purpose of developing a fluent and human-like collaborative robot, qualitative and specific quantitative metrics are required, in order to interpret where and how can the system be improved and which methodologies should be refined or even replaced by new ones. To tackle this concern, ref. [109] proposes a mixture between objective and subjective metrics to evaluate human-robot object handover interactions. The subjective metrics were formulated based on a score that each different participant gave after the experience of changing objects with the robot. Each participant had to evaluate four distinct aspects: how easy was to receive the object; how satisfied they are with the interaction; how comfortable was the interaction and how safe they felt during the interaction. Additionally, ref.