In an experimental study of humans reactions to social motor intention in a humanoid robot, we showed that SMI cause the emergence of social interaction between human and robot. We investigated whether people would respond differently to a humanoid robot depending on the kinematic profile of its movement. A robot placed a block on a table in front of a human subject in three different ways. We designed the robot’s arm and upper body movements to manifest the human kinematic profile (...) of either a non-social motor intention or a social motor intention. In the control condition the robot performed an irregular movements. Once the robot had finished its task, the task of the human was to place another block on top of the first one. We distinguishedbetween social and non-social responses to the robot’s behavior based in gaze behavior and kinematic profile of the human’s arm movement during the task. Our results show that the behavior of the human can be modulated by the kinematics of a robot’s motor action. In several cases the participants reciprocated movements displaying social motor intention with movements with a similar kind of kinematics, attempting to make eye contact during the task. This shows that HRI can emerge implicitly by sensorimotor processing and suggests that implementing a mechanism for social-motor intention in social robots designed to interact spontaneously would be useful. (shrink)

We propose that moral behaviour of artificial agents could be intrinsically grounded in their own sensory-motor experiences. Such an ability depends critically on seven types of competencies. First, intrinsic morality should be grounded in the internal values of the robot arising from its physiology and embodiment. Second, the moral principles of robots should develop through their interactions with the environment and with other agents. Third, we claim that the dynamics of moral emotions closely follows that of other non-social emotions used (...) in valuation and decision making. Fourth, we explain how moral emotions can be learned from the observation of others. Fifth, we argue that to assess social interaction, a robot should be able to learn about and understand responsibility and causation. Sixth, we explain how mechanisms that can learn the consequences of actions are necessary for a robot to make moral decisions. Seventh, we describe how the moral evaluation mechanisms outlined can be extended to situations where a robot should understand the goals of others. Finally, we argue that these competencies lay the foundation for robots that can feel guilt, shame and pride, that have compassion and that know how to assign responsibility and blame. (shrink)

Reinforcement learning systems usually assume that a value function is defined over all states (or state-action pairs) that can immediately give the value of a particular state or action. These values are used by a selection mechanism to decide which action to take. In contrast, when humans and animals make decisions, they collect evidence for different alternatives over time and take action only when sufficient evidence has been accumulated. We have previously developed a model of memory processing that includes semantic, (...) episodic and working memory in a comprehensive architecture. Here, we describe how this memory mechanism can support decision making when the alternatives cannot be evaluated based on immediate sensory information alone. Instead we first imagine, and then evaluate a possible future that will result from choosing one of the alternatives. Here we present an extended model that can be used as a model for decision making that depends on accumulating evidence over time, whether that information comes from the sequential attention to different sensory properties or from internal simulation of the consequences of making a particular choice. We show how the new model explains both simple immediate choices, choices that depend on multiple sensory factors and complicated selections between alternatives that require forward looking simulations based on episodic and semantic memory structures. In this framework, vicarious trial and error is explained as an internal simulation that accumulates evidence for a particular choice. We argue that a system like this forms the “missing link” between more traditional ideas of semantic and episodic memory, and the associative nature of reinforcement learning. (shrink)