Predictive Mechanisms Are Not Involved the Same Way during Human-Human vs. Human-Machine Interactions: A Review <s> Predictive Systems for Shared Action with Machines <s> In the future, there will be many cases where robots should perform cooperative tasks with humans in daily life. We focus on a hand-over motion as an example of cooperative work between a human and a robot, and propose an algorithm which enables a robot to perform a human-like motion. First we analyze trajectories and velocity patterns of a hand-over motion performed by two humans. From this experimental results we notice that a receiver's motion, during hand-over has some typical characteristics. We then confirm that a human-like motion can be generated using these characteristics. Finally, we plan the robot's motion considering these results. Initially, two kinds of potential fields are used to generate a motion command which leads the robot along a trajectory similar to that followed by the human. In addition, more precise motion is considered at the end of the hand-over operation to guarantee accurate positioning and to soften the shock of contact. Experimental results using 2-link planar robot arm show the validity of the proposed method. <s> BIB001 </s> Predictive Mechanisms Are Not Involved the Same Way during Human-Human vs. Human-Machine Interactions: A Review <s> Predictive Systems for Shared Action with Machines <s> Awareness of actions is partly based on the intentions accompanying them. Thus, the awareness of self- and other-generated actions should differ to the extent that access to own and other's intentions differs. Recent studies have found a brain circuit (the mirrorneuron system) that represents self- and other-generated actions in an integrated fashion. This system does not respond to actions made by nonagents, such as machines. We measured the estimated onset time of actions that subjects either executed themselves or observed being executed by someone else or by a machine. In three experiments, the estimates of the machine actions always differed from those of self- and other-generated actions, whereas the latter two were indistinguishable. Our results are consistent with the view that intentions are attributed to others but not to machines. They also raise the interesting possibility that people attribute intentions to themselves in the same way as they do to others. <s> BIB002 </s> Predictive Mechanisms Are Not Involved the Same Way during Human-Human vs. Human-Machine Interactions: A Review <s> Predictive Systems for Shared Action with Machines <s> A long-standing question within the robotics community is about the degree of human-likeness robots ought to have when interacting with humans. We explore an unexamined aspect of this problem: how people empathize with robots along the anthropomorphic spectrum. We conducted an experiment that measured how people empathized with robots shown to be experiencing mistreatment by humans. Our results indicate that people empathize more strongly with more human-looking robots and less with mechanical-looking robots. <s> BIB003 </s> Predictive Mechanisms Are Not Involved the Same Way during Human-Human vs. Human-Machine Interactions: A Review <s> Predictive Systems for Shared Action with Machines <s> When we have to physically interact with a robot, the benchmark for natural and efficient performance is our experience of daily interactions with other humans. This goal is still far despite significant advances in human-robot interaction. While considerable progress is made in various areas ranging from improving the hardware over safety measures to better sensor systems, the research on basic mechanisms of interaction and its technical implementation is still in its infancy. In the following, we give an overview of our own work aiming at improving human-robot interaction and joint action. When humans jointly collaborate to achieve a common goal, the actions of each partner need to be properly coordinated to assure a smooth and efficient workflow. This includes timing of the actions, but also, in the case of physical interaction, the spatial coordination. We thus first investigated how a simple physical interaction, a hand-over task between two humans without verbal communication, is achieved. Our results with a human as receiver and both humans and robots as delivering agent show that both the position and the kinematics of the partner's movement are used to increase the confidence in predicting hand-over in time and space. These results underline that for successful joint action the robot must act predictably for the human partner. However, in a more realistic scenario, robot and human constitute a dynamic system with each agent predicting and reacting to the actions and intentions of the other. We therefore investigated and implemented an assembly task where the robot acts as assistant for the human. Using a Bayesian estimation framework, the robot predicts assembly duration in order to deliver the next part just in time. <s> BIB004
Robotic research has been deeply interested in optimizing human-machine interactions in order to enhance efficiency and safety. So far, studies had involved a large variety of different automated systems with varying complexity, from computers to robotic arms, from levers to full humanoids (see Figure 5 for an overview). Yet, there has been a trend toward machine humanization during these recent years. While some researchers took an interest in humanizing the external appearance of the machine BIB002 BIB003 , others have focused on its motor characteristics BIB001 BIB004 . Interestingly, current research tends to show that human-like machine-generated actions can engage the same predictive mechanisms that are brought into play during human interactions whereas this ability remain impaired during interactions with traditional automata.