# Leader-Follower Role Allocation for Physical Collaboration in Human Dyads
## 4 Discussion
This matches what we expect with Fitts' law as Equation (2) states that the difficulty of each target directly depends on its size and distance, which subsequently affects the calculated index of performance or IP. Contributions and future workResults presented in this study can be seen as beneficial in better understanding of collaborative dynamics that present themselves in human collaboration as well as in the further development of novel robot control models. Implementing dynamics found in human collaboration aids in the further improvement in human-robot collaboration as the human partners find human-like behaviour of robots more intuitive, as shown in [18; 25]. Furthermore, a study by [PERSON] et al. [26] found that humans prefer a robot partner with human-like behaviour to an actual human partner for motion assistance as they are more predictable than humans. In this regard, many human-based control models have already been developed, such as [27; 28; 29; 30; 31], however, these did not take into account the leader-follower dynamics found in human collaboration. Instead, in the accounted human-based control models the robot partner always assumes the role of the follower. Implementing the leader-follower role allocation studied in this paper into a robot control model could on the other hand allow the robot partner to assume both the role of a follower and that of a leader when necessary. The importance of role distributions in human-robot collaboration has also been addressed in [32]. In addition, human-based control models for robots are regularly used in social robotics for effective physical rehabilitation as seen in [33]. In this regard, the leader-follower role allocation presented in this study can as well be beneficial when incorporated in the design of systems that aid in human skill learning for either human or human-robot dyads. Studies by [PERSON] et al. in [34] and [PERSON] et al. in [35] showed that practising with a peer was more advantageous to the subject than with an expert. Knowing this, the latter study suggested that the results show the possibility of teaching an expert user to be an expert teacher as the expert teacher must learn how to support, instead of explicitly leading the naive user, in order to leave room for the naive user to learn. Having this in mind, the results presented in this paper have the potential to be used for matching best-suited individuals together based on their solo performance to assure effective skill learning. In the same regard the results can be incorporated in the design of a human-robot collaborative system that is able to support the human user in more effective skill learning by adjusting its influence on the task performance. However, to do so, in future work the experiments should be performed so as to be able to observe skill learning of both partners through time. Furthermore, it would be beneficial to examine whether the leader-followerdynamics observed in this study are maintained in new, more complex study cases such as 3D tasks. **Supplementary Materials:** The following are available online at [[https://www.mdpi.com/article/10.3390/app11198928/s1](https://www.mdpi.com/article/10.3390/app11198928/s1)]([https://www.mdpi.com/article/10.3390/app11198928/s1](https://www.mdpi.com/article/10.3390/app11198928/s1)): Table of data used in this study, including calculated metrics, in.mat and.csv format. **Author Contributions:** Conceptualization, R.K.L., J.C. and T.P. ; methodology, R.K.L., J.C. and T.P. ; software, R.K.L. ; validation, R.K.L., J.C. and T.P. ; formal analysis, R.K.L. and J.C.; investigation, R.K.L. ; resources, T.P. ; data curation, R.K.L. ; writing--original draft preparation, R.K.L. ; writing--review and editing, J.C. and T.P. ; visualization, R.K.L. ; supervision, T.P. ; project administration, T.P. ; funding acquisition, T.P. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by Slovenian Research Agency grant N2-0130.