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 field of HRI has been the focus of many research efforts during the last few decades. A key observation is that, successful human-robot collaborations requires that the human has to understand the robot and, simultaneously, that the robot is also able to understand the human actions @cite_8 . This has propelled fields such as robotics, computer vision, human cognition, psychology, and neuroscience, to combine resources in the aim of proposing techniques that tackle those intertwined issues.
@cite_8
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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. Human-robot interaction (HRI) is a key element for diffusion of robotised production. Clear advantages in flexibility and productivity are possible, when the two operators are free to interact, as they are endowed with complementary skills. To achieve such a goal, safety systems capable of coping with task and robot constraints have to be designed. In this paper, a collision avoidance strategy, tackling consistency with task constraints and robot kinematic limitations, is proposed. Robot joint velocities are selected with a QP optimisation problem, minimising the difference from evasive velocities, while respecting task constraints. Integration with an industrial controller is discussed as well, while the strategy is experimentally validated on a dual arm industrial robot prototype, working in close interaction with a human. This article presents the results of video-based Human Robot Interaction (HRI) trials which investigated people's perceptions of different robot appearances and associated attention-seeking features and behaviors displayed by robots with different appearance and behaviors. The HRI trials studied the participants' preferences for various features of robot appearance and behavior, as well as their personality attributions towards the robots compared to their own personalities. Overall, participants tended to prefer robots with more human-like appearance and attributes. However, systematic individual differences in the dynamic appearance ratings are not consistent with a universal effect. Introverts and participants with lower emotional stability tended to prefer the mechanical looking appearance to a greater degree than other participants. It is also shown that it is possible to rate individual elements of a particular robot's behavior and then assess the contribution, or otherwise, of that element to the overall perception of the robot by people. Relating participants' dynamic appearance ratings of individual robots to independent static appearance ratings provided evidence that could be taken to support a portion of the left hand side of Mori's theoretically proposed uncanny valley' diagram. Suggestions for future work are outlined. Objective:The current status of human–robot interaction (HRI) is reviewed, and key current research challenges for the human factors community are described.Background:Robots have evolved from continuous human-controlled master–slave servomechanisms for handling nuclear waste to a broad range of robots incorporating artificial intelligence for many applications and under human supervisory control.Methods:This mini-review describes HRI developments in four application areas and what are the challenges for human factors research.Results:In addition to a plethora of research papers, evidence of success is manifest in live demonstrations of robot capability under various forms of human control.Conclusions:HRI is a rapidly evolving field. Specialized robots under human teleoperation have proven successful in hazardous environments and medical application, as have specialized telerobots under human supervisory control for space and repetitive industrial tasks. Research in areas of self-driving cars, intimate co...