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.
The work discussed above has focused on either intrapersonal coupling, synchronization, and action understanding from the gaze behavior of two agents. Notwithstanding, understanding of the arm movements in humans and robots is also a focus of extensive work. @cite_24 have created a framework for evaluating predictable and legible arm movements for humans and robots. Where legibility is am exaggerated motion towards the end-goal, and the predictable is a natural human movement. Nonetheless, there is no guarantee of generalization for all types of scenarios. yet no understanding whether this is present in HHI, and in retrospect, HRI scenarios. Although, to improve social interactions, robots and humans should mutually influence each other @cite_26 .
@cite_24 @cite_26
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Interest in human-robot coexistence, in which humans and robots share a common work volume, is increasing in manufacturing environments. Efficient work coordination requires both awareness of the human pose and a plan of action for both human and robot agents in order to compute robot motion trajectories that synchronize naturally with human motion. In this paper, we present a data-driven approach that synthesizes anticipatory knowledge of both human motions and subsequent action steps in order to predict in real-time the intended target of a human performing a reaching motion. Motion-level anticipatory models are constructed using multiple demonstrations of human reaching motions. We produce a library of motions from human demonstrations, based on a statistical representation of the degrees of freedom of the human arm, using time series analysis, wherein each time step is encoded as a multivariate Gaussian distribution. We demonstrate the benefits of this approach through offline statistical analysis of human motion data. The results indicate a considerable improvement over prior techniques in early prediction, achieving 70 or higher correct classification on average for the first third of the trajectory (< 500msec). We also indicate proof-of-concept through the demonstration of a human-robot cooperative manipulation task performed with a PR2 robot. Finally, we analyze the quality of task-level anticipatory knowledge required to improve prediction performance early in the human motion trajectory. 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 In order for humans and robots to interact in an effective and intuitive manner, robots must obtain information about the human affective state in response to the robot's actions. This secondary mode of interactive communication is hypothesized to permit a more natural collaboration, similar to the "body language" interaction between two cooperating humans. This paper describes the implementation and validation of a hidden Markov model (HMM) for estimating human affective state in real time, using robot motions as the stimulus. Inputs to the system are physiological signals such as heart rate, perspiration rate, and facial muscle contraction. Affective state was estimated using a two- dimensional valence-arousal representation. A robot manipulator was used to generate motions expected during human-robot interaction, and human subjects were asked to report their response to these motions. The human physiological response was also measured. Robot motions were generated using both a nominal potential field planner and a recently reported safe motion planner that minimizes the potential collision forces along the path. The robot motions were tested with 36 subjects. This data was used to train and validate the HMM model. The results of the HMM affective estimation are also compared to a previously implemented fuzzy inference engine. In a typical Human-Robot Interaction (HRI) scenario, the robot needs to perform various tasks for the human, hence should take into account human oriented constraints. In this context it is not sufficient that the robot selects grasp and placement of the object from the stability point of view only. Motivated from human behavioral psychology, in this paper we emphasize on the mutually depended nature of grasp and placement selections, which is further constrained by the task, the environment and the human's perspective. We will explore essential human oriented constraints on grasp and placement selections and present a framework to incorporate them in synthesizing key configurations of planning basic interactive manipulation tasks.