Self discovery enables robot social cognition: are you my teacher?

Bootstrapping Virtual Bipedal Walkers with Robotics Scaffolded Learning

We reach walking optimality from a very early age by using natural supports, which can be the hands of our parents, chairs, and training wheels, and bootstrap a new knowledge from the recently acquired one. The idea behind bootstrapping is to use the previously acquired knowledge from simpler tasks to accelerate the learning of more complicated ones. In this paper, we propose a scaffolded learning method from an evolutionary perspective, where a biped creature achieves stable and independent bipedal walking while exploiting the natural scaffold of its changing morphology to create a third limb. The novelty of this work is speeding up the learning process with an artificially recreated scaffolded learning. We compare three conditions of scaffolded learning (free, time-constrained, and performance-based scaffolded learning) to reach bipedalism, and we prove that a performance-based scaffold, which is designed by the walking velocity obtained, is the most conducive to bootstrap the learning of bipedal walking. The scope of this work is not to study bipedal locomotion but to investigate the contribution from scaffolded learning to a faster learning process. Beyond a pedagogical experiment, this work presents a powerful tool to accelerate the learning of complex tasks in the Robotics field.

Digital life, a theory of minds, and mapping human and machine cultural universals

Emerging cybertechnologies, such as social digibots, bend epistemological conventions of life and culture already complicated by human and animal relationships. Virtually-augmented niches of machines and organic life promise new free-energy-governed selection of intelligent digital life. These provocative eco-evolutionary contexts demand a theory of (natural and artificial) minds to characterize and validate the immersive social phenomena universally-shaping cultural affordances.

The humanness of artificial non-normative personalities

Technoscientific ambitions for perfecting human-like machines, by advancing state-of-the-art neuromorphic architectures and cognitive computing, may end in ironic regret without pondering the humanness of fallible artificial non-normative personalities. Self-organizing artificial personalities individualize machine performance and identity through fuzzy conscientiousness, emotionality, extraversion/introversion, and other traits, rendering insights into technology-assisted human evolution, robot ethology/pedagogy, and best practices against unwanted autonomous machine behavior.

A Bayesian Developmental Approach to Robotic Goal-Based Imitation Learning

A fundamental challenge in robotics today is building robots that can learn new skills by observing humans and imitating human actions. We propose a new Bayesian approach to robotic learning by imitation inspired by the developmental hypothesis that children use self-experience to bootstrap the process of intention recognition and goal-based imitation. Our approach allows an autonomous agent to: (i) learn probabilistic models of actions through self-discovery and experience, (ii) utilize these learned models for inferring the goals of human actions, and (iii) perform goal-based imitation for robotic learning and human-robot collaboration. Such an approach allows a robot to leverage its increasing repertoire of learned behaviors to interpret increasingly complex human actions and use the inferred goals for imitation, even when the robot has very different actuators from humans. We demonstrate our approach using two different scenarios: (i) a simulated robot that learns human-like gaze following behavior, and (ii) a robot that learns to imitate human actions in a tabletop organization task. In both cases, the agent learns a probabilistic model of its own actions, and uses this model for goal inference and goal-based imitation. We also show that the robotic agent can use its probabilistic model to seek human assistance when it recognizes that its inferred actions are too uncertain, risky, or impossible to perform, thereby opening the door to human-robot collaboration.

"Social" robots are psychological agents for infants: a test of gaze following

Gaze following is a key component of human social cognition. Gaze following directs attention to areas of high information value and accelerates social, causal, and cultural learning. An issue for both robotic and infant learning is whose gaze to follow. The hypothesis tested in this study is that infants use information derived from an entity's interactions with other agents as evidence about whether that entity is a perceiver. A robot was programmed so that it could engage in communicative, imitative exchanges with an adult experimenter. Infants who saw the robot act in this social-communicative fashion were more likely to follow its line of regard than those without such experience. Infants use prior experience with the robot's interactions as evidence that the robot is a psychological agent that can see. Infants want to look at what the robot is seeing, and thus shift their visual attention to the external target.