The scientific objectives of the outgoing phase were to identify temporal adaptation and anticipation mechanisms in human-human interactions characterized by errors. This objective aimed to answer the question: What are the mechanisms that allow humans to stay “inside the loop” when interacting with other human agents? A key outcome was supposed to identify how and to which extent temporal adaptation and anticipation mechanisms are crucial to ensure the co-agents' performance monitoring and its successful prediction of and recovery from errors. To this end, the ER has designed and implemented an experiment using musical paradigms aiming to evaluate how a leader-follower pair of individuals adapt their performance to reduce the negative impact of errors on coordination during a musical task. Results showed when errors were included in the task, participants’ performance was characterized by lower variability in asynchrony and higher interpersonal coupling compared to correct sequences, suggesting that humans succeed in stay-in-the-loop by adopting a top-down control over sensorimotor mechanisms of coordination to reduce the impact of anticipations. The scientific objectives of the returning phase were to develop a module for humanoid robots that ensures real-time coordination and (iv) test its efficiency in reducing the OOTL. These objectives aimed to answer the question: Does endowing robots with mutual adaptation improve users’ performance of monitoring their own and robot's actions, as well as the outcome of their combined actions when performing joint actions together? To this end, the ER has implemented the human-inspired computational model for temporal adaptation and anticipation (ADAM) on the humanoid robot iCub. Specifically, the ER designed and implemented a study aiming to evaluate whether implementing in robots temporal adaptation and anticipation processes that facilitate coordination in humans can reduce the OOTL phenomenon in HRI. To this end, human participants were asked to play a musical joint tapping task together with the iCub robot. Specifically, two versions of the task were developed. A version in which the iCub played always the same sequence played by human participants (Correct task condition) and a version of the task in which in 30% of the repetitions, the iCub played a mismatching sequence (Erring task condition). For both task conditions, we implemented the ADAM and integrated it with the robot to control iCub’s tapping behaviour. In both task conditions, 5 different settings of the ADAM model were implemented, two of them relying only on the adaptive module and three relying on the joint module. Results showed that that endowing robots with temporal adaptation and anticipation mechanisms allows humans to stay in-the-loop, in two different ways. Firstly, it allows users to constantly update their internal model of the robot to reduce the prediction error about iCub’s performance, thus resulting in higher synchronization and more strength in coupling for those conditions in which the robot is run based on human-like estimates. Secondarily, by increasing self-other integration and the perception of shared agency.
