On the evolution of behaviors through embodied imitation

© 2015 Massachusetts Institute of Technology. Abstract This article describes research in which embodied imitation and behavioral adaptation are investigated in collective robotics. We model social learning in artificial agents with real robots. The robots are able to observe and learn each others' movement patterns using their on-board sensors only, so that imitation is embodied. We show that the variations that arise from embodiment allow certain behaviors that are better adapted to the process of imitation to emerge and evolve during multiple cycles of imitation. As these behaviors are more robust to uncertainties in the real robots' sensors and actuators, they can be learned by other members of the collective with higher fidelity. Three different types of learned-behavior memory have been experimentally tested to investigate the effect of memory capacity on the evolution of movement patterns, and results show that as the movement patterns evolve through multiple cycles of imitation, selection, and variation, the robots are able to, in a sense, agree on the structure of the behaviors that are imitated

Influencing robot learning through design and social interactions: a framework for balancing designer effort with active and explicit interactions

This thesis examines a balance between designer effort required in biasing a robot’s learn-ing of a task, and the effort required from an experienced agent in influencing the learning using social interactions, and the effect of this balance on learning performance. In order to characterise this balance, a two dimensional design space is identified, where the dimensions represent the effort from the designer, who abstracts the robot’s raw sensorimotor data accord-ing to the salient parts of the task to increasing degrees, and the effort from the experienced agent, who interacts with the learner robot using increasing degrees of complexities to actively accentuate the salient parts of the task and explicitly communicate about them. While the in-fluence from the designer must be imposed at design time, the influence from the experienced agent can be tailored during the social interactions because this agent is situated in the environ-ment while the robot is learning. The design space is proposed as a general characterisation of robotic systems that learn from social interactions. The usefulness of the design space is shown firstly by organising the related work into the space, secondly by providing empirical investigations of the effect of the various influences o

A Posture Sequence Learning System for an Anthropomorphic Robotic Hand

The paper presents a cognitive architecture for posture learning of an anthropomorphic robotic hand. Our approach is aimed to allow the robotic system to perform complex perceptual operations, to interact with a human user and to integrate the perceptions by a cognitive representation of the scene and the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces and to perform complex interaction with the human operator

Measuring and Modulating Mimicry: Insights from Virtual Reality and Autism

Mimicry involves the unconscious imitation of other people’s behaviour. The social top-down response modulation (STORM) model has suggested that mimicry is a socially strategic behaviour which is modulated according to the social context, for example, we mimic more when someone is looking at us or if we want to affiliate with them. There has been a long debate over whether mimicry is different in autism, a condition characterised by differences in social interaction. STORM predicts that autistic people can and do mimic but do not change their mimicry behaviour according to the social context. Using a range of mimicry measures this thesis aimed to test STORM’s predictions. The first study employed a traditional reaction time measure of mimicry and demonstrated that direct gaze socially modulated mimicry responses in non-autistic adults but did not do so in autistic participants, in line with STORM’s predictions. In the next two studies, I found that non-autistic participants mimicked the movement trajectory of both virtual characters and human actors during an imitation game. Autistic participants also mimicked but did so to a lesser extent. However, this type of mimicry was resistant to the effects of social cues, such as eye-gaze and animacy, contrary to the predictions of STORM. In a fourth study, I manipulated the rationality of an actor’s movement trajectory and found that participants mimicked the trajectory even when the trajectory was rated as irrational. In a fifth study, I showed that people’s tendency to mimic the movements of others could change the choices that participants had previously made in private. This tendency was modulated by the kinematics of the character’s pointing movements. This thesis provides mixed support for STORM’s predictions and I discuss the reasons why this might be. I also make suggestions for how to better measure and modulate mimicry

Bridging the gap between emotion and joint action

Our daily human life is filled with a myriad of joint action moments, be it children playing, adults working together (i.e., team sports), or strangers navigating through a crowd. Joint action brings individuals (and embodiment of their emotions) together, in space and in time. Yet little is known about how individual emotions propagate through embodied presence in a group, and how joint action changes individual emotion. In fact, the multi-agent component is largely missing from neuroscience-based approaches to emotion, and reversely joint action research has not found a way yet to include emotion as one of the key parameters to model socio-motor interaction. In this review, we first identify the gap and then stockpile evidence showing strong entanglement between emotion and acting together from various branches of sciences. We propose an integrative approach to bridge the gap, highlight five research avenues to do so in behavioral neuroscience and digital sciences, and address some of the key challenges in the area faced by modern societies

ARTIFICIAL INTELLIGENCE AND THE TECHNOLOGICAL SUBLIME: HOW VIRTUAL CHARACTERS INFLUENCE THE LANDSCAPE OF MODERN SUBLIMITY

The principle objective of this thesis is to expand the term “technological sublime” to include technologies of artificial intelligence. In defining new realms of the technological sublime, we must not only consider the ecological integration of technology within natural surroundings, but also appreciate modern technological objects that instigate sublime experiences. This work examines science fictional portrayals of interactions with sentient artificial intelligence in I, Robot, 2001: A Space Odyssey and other major works of science fiction. In each of these works, characters who encounter technologies possessing artificial intelligence share sublime experiences. This thesis considers various levels of embodiment associated with the objects of artificial intelligence and discusses the sublime qualities of both cybernetic and android beings. Finally, this work examines how our perceptions of environment are altered by the introduction of virtual reality and virtual landscapes, which consequently affects our mindscapes and contribute to the technological sublime

Gendered AI: German news media discourse on the future of work

In recent years, there has been a growing public discourse regarding the influence AI will have on the future of work. Simultaneously, considerable critical attention has been given to the implications of AI on gender equality. Far from making precise predictions about the future, this discourse demonstrates that new technologies are instances for renegotiating the relation of gender and work. This paper examines how gender is addressed in news media discourse on AI and the future of work, focusing on Germany. We approach this question from a perspective of feminist technology studies and discourse analysis, exploring a corpus of 178 articles from 2015 to 2021 from German newspapers and newsmagazines. The findings indicate that critical AI and gender knowledge circulates in public discourse in the form of specific discursive frames, thematizing algorithmic bias, automatization and enhancement, and gender stereotypes. As a result, we show that, first, the discourse takes up feminist and scholarly discourse on gender and discusses AI in a way that is informed by social constructivism and standpoint theories. Second, gender appears as a—to some extent intersectional—diversity category which is critical to AI, while at the same time omitting important perspectives. Third, it can be shown that there is a renegotiating of the ideal worker norm taking place, and finally, we argue that the gendered frame of the powerful men developer responsible for AI’s risk is a concept to be challenged