The paper provides a summary of our recent research on preverbal infants (using violation-of-expectation and observational learning paradigms) demonstrating that one-year-olds interpret and draw systematic inferences about other's goal-directed actions, and can rely on such inferences when imitating other's actions or emulating their goals. To account for these findings it is proposed that oneyear -olds apply a non-mentalistic action interpretational system, the 'teleological stance' that represents actions by relating relevant aspects of reality (action, goal-state, and situational constraints) through the principle of rational action, which assumes that actions function to realize goal-states by the most efficient means available in the actor's situation. The

This experiment investigated 12-month-old infants’ ability to link an event’s beginning to its probable ending. Following Csibra, Biro, Koos, and Gergely (2003), infants were habituated to a simple chasing event involving animated balls, and at test saw 2 possible endings: either 1 ball caught the other or failed to do so. Two controls Do Not Copy were added to the previous work. First, the total amount of motion was controlled in the test endings; second, the endings were paired with a nonchasing beginning to ensure that behavior at test reflected representation of the event beginning itself. The results replicated Csibra et al.’s finding that infants look longer at the noncatching ending following the chasing beginning; moreover, infants showed no preference for either ending following the no-chasing beginning. This study supports the claim that infants can calculate the rational ending of a goal-directed motion event.

Adults and even human infants readily interpret the actions of others as goal-directed, but the developmental origins of this important precursor of a theory of mind is hotly debated. According to one view, infants acquire an understanding of actions as goal-directed through gradual experience with particular actions of human agents (Poulin-Dubois, 1999, Tomasello, 1999; Woodward, 1998). An alternative interpretation for the understanding of goaldirected actions holds that infants ’ reasoning about actions as goal-directed is based on innate specialized systems that are sensitive to abstract behavioral cues and attribute goals to a wide range of entities (Csibra & Gergely, 1998). Such cues include morphological features, biological motion, self-propulsion, and

to the special issue Our behavior is commonly much more driven by our intentions and goals than by particular stimuli or situations. That is, we are used to experiencing and interpreting goal-directed actions of others, and carrying out goal-directed actions by ourselves. This special

This dissertation presents an approach to robot programming by demonstration based on two key concepts: demonstrator intent is the most meaningful signal that the robot can observe, and the robot should have a basic level of behavioral competency from which to interpret observed actions. Intent is a teleological, robust teaching signal invariant to many common sources of noise in training. The robot can use the knowledge encapsulated in sensorimotor schemas to interpret the demonstration. Furthermore, knowledge gained in prior demonstrations can be applied to future sessions. iv I argue that programming by demonstration be organized into declarative and procedural components. The declarative component represents a reusable outline of underlying behavior that can be applied to many different contexts. The procedural component represents the dynamic portion of the task that is based on features observed at run time. I describe how statistical models, and Bayesian methods in particular, can be used to model these components. These models have many features that are beneficial for learning in this domain, such as tolerance for uncertainty, and the ability to incorporate prior knowledge into inferences. I demonstrate this architecture through experiments on a bimanual humanoid robot using tasks from the pick and place domain.

We are victims of an illusion that makes us believe that we have created what actually grasps our volition from without. Durkheim: The rules of sociological method (1895/1982, p. 13) Abstract. The paper starts from a general consideration of three programs in cognitive science: the internalist, the externalist, and the social approaches to cognition. In the social domain, some new approaches propose that human sociality is to be treated as part of our biological nature. Several research programs were born out of these considerations. There are some among them that propose general theories for the distribution of representations. The paper analyses two of these, the meme theory put forward by RICHARD DAWKINS, and the epidemiological theory proposed by DAN SPERBER. It points out that while for DAWKINS the essential aspect is replication, for SPERBER it is transmission of representations where biological analogies become crucial. For both theories, to turn them into working models, a lot of detailed elaboration is needed from data on social science.

Abstract—A key requirement for seamless human-robot collaboration is for the robot to make its intentions clear to its human collaborator. A collaborative robot’s motion must be legible, or intent-expressive. Legibility is often described in the literature as and effect of predictable, unsurprising, or expected motion. Our central insight is that predictability and legibility are fundamentally different and often contradictory properties of motion. We develop a formalism to mathematically define and distinguish predictability and legibility of motion. We formalize the two based on inferences between trajectories and goals in opposing directions, drawing the analogy to action interpretation in psychology. We then propose mathematical models for these inferences based on optimizing cost, drawing the analogy to the principle of rational action. Our experiments validate our formalism’s prediction that predictability and legibility can contradict, and provide support for our models. Our findings indicate that for robots to seamlessly collaborate with humans, they must change the way they plan their motion. Keywords—human-robot collaboration, motion planning, trajectory optimization, formalism, manipulation, action interpretation I.