Human interaction involves very sophisticated non-verbal communication skills like understanding the goals and actions of others and coordinating our own actions accordingly. Neuroscience refers to this mechanism as motor resonance, in the sense that the perception of another person's actions and sensory experiences activates the observer's brain as if (s)he would be performing the same actions and having the same experiences. We analyze and model non-verbal cues (arm movements) exchanged between two humans that interact and execute handover actions. The contributions of this paper are the following: (i) computational models, using recorded motion data, describing the motor behaviour of each actor in action-in-interaction situations, (ii) a computational model that captures the behaviour if the "giver" and "receiver" during an object handover action, by coupling the arm motion of both actors, and (iii) embedded these models in the iCub robot for both action execution and recognition. Our results show that: (i) the robot can interpret the human arm motion and recognize handover actions; and (ii) behave in a "human-like" manner to receive the object of the recognized handover action.
@cite_14: Cognitive neuroscience experiments show how people intensify the exchange of non-verbal cues when they work on a joint task towards a common goal. When individuals share their intentions, it creates a social interaction that drives the mutual alignment of their actions and behavior. To understand the intentions of others, we strongly rely on the gaze cues. According to the role each person plays in the interaction, the resulting alignment of the body and gaze movements will be different. This mechanism is key to understand and model dyadic social interactions.
@cite_16: Future robots must co-exist and directly interact with human beings. Designing these agents imply solving hard problems linked to human-robot interaction tasks. For instance, how a robot can choose an interacting partner among various agents and how a robot locates regions of interest in its visual field. Studies of neurobiology and psychology collectively named synchrony as an indispensable parameter for social interaction. We assumed that Human-Robot interaction could be initiated by synchrony detection. In this paper, we present a developmental approach for analyzing unintentional synchronization in human-robot interaction. Using our neural network model, the robot learns from a babbling step its inner dynamics by associating its own motor activities (oscillators) with the visual stimulus induced by its own motion. After learning the robot is capable of choosing an interacting agent and of localizing the spatial position of its preferred partner by synchrony detection.
@cite_26: Here, we propose that bidirectionality in implicit motor coordination between humanoid robots and humans could enhance the social competence of human–robot interactions. We first detail some questions pertaining to human–robot interactions, introducing the Uncanny Valley hypothesis. After introducing a framework pertinent for the understanding of natural social interactions, motor resonance, we examine two behaviors derived from this framework: motor coordination, investigated in and informative about human–human interaction, and motor interference, which demonstrate the relevance of the motor resonance framework to describe human perception of humanoid robots. These two lines of investigation are then put together to “close the loop” by proposing to implement a key feature of motor coordination, bidirectionality, in robots’ behavior. Finally, we discuss the feasibility of implementing motor coordination between humanoid robots and humans, and the consequences of this implementation in enhancing the social competence of robots interacting with humans. Keywords: interpersonal interaction, motor resonance, motor coordination, motor interference, social robotics, anthropomorphism
Rakovi ' c et al @cite_16 models the gaze behavior interdependencies for two agents using Hidden Markov Models (HMM) where the states are the gaze fixations of one agent, and the observations are the gaze fixations of the other. HMM then estimates the valid gaze behavior of the agent observing the action, and the correct action performed by the other agent. This is used to build a controller which permits the robot (observer follower) to read the non-verbal communication cues from the gaze of the human participant (leader of the action) and infer the intended action. On another note, @cite_26 state that to improve social interactions, robots and humans should mutually influence each other. As such, @cite_14 worked on action alignment between humans and robots. Where the robot is the leader, and in order to correctly adapt to the intricacies of collaborating with a human, a Discrete Time Markov Model is used to give information whether the human is understanding the action of the robot.