Towards robot cultures?

Abstract

The study of imitation and other mechanisms of social learning is an exciting area of research for all those interested in understanding the origin and the nature of animal learning in asocial context. Moreover, imitation is an increasingly important research topic in Artificial Intelligence and social robotics which opens up the possibility ofindividualized social intelligencein robots that are part of a community, and allows us to harness not only individual learning by the single robot, but also the acquisition of new skills by observing other members of the community (robots, humans, or virtual agents). After an introduction to the main research issues in research on imitation in various fields (including psychology, biology and robotics), we motivate the particular focus of this work, namely thecorrespondence problem. We describeAction Learning for Imitation via Correspondences between Embodiments(Alice), an implemented generic framework for solving the correspondence problem between differently embodied robots. Alice enables a robotic agent to learn a behavioral repertoire suitable to performing a task by observing a model agent. Importantly, the model agent could possibly possess a different type of body, e.g. a different number of limbs or joints (implying different degrees of freedom), a different height, different sensors, a different basic action repertoire, etc. Previously, in a test-bed where the agents differed according to their possible movement patterns, we demonstrated that the character of imitation achieved will depend on the granularity of subgoal matching, and on the metrics used to evaluate success.In our current work, we implemented Alice in a new test-bed calledRabitwhere simple simulated robotic arm agents use various metrics for evaluating success according to actions, states, effects, or weighted combinations.We examine the roles ofsynchronization,looseness of perceptual matching, and ofproprioceptive matchingby a series of experiments. Also, we study how Alice copes withchanges in the embodimentof the imitator during learning. Our simulation results suggest thatsynchronizationandloose perceptual matchingallow for faster acquisition of behavioral compentencies at low error rates.Social learning (broadly construed) plays a role as areplicationmechanism for behaviors and results invariabilitywhen the transmitted behavior differs from the model’s behavior, thus providing theevolutionary substrate for cultureand its pre-cursors. Social learning in robotics could therefore serve as the basis for culture in societies whose members include artificial agents. We address the use of imitative social learning mechanisms like Alice for transmission of skills between robots, and give first examples of transmission of a skill despite differences in embodiment of agents involved. In the particular setup, transmission occurs through a chain, as well as emerging in cyclic arrangements of robots. These simple examples demonstrate that by using social learning and imitation,(proto-)cultural transmissionis possible among robots, even in heterogeneous groups of robots.

On the Evolution of Behaviors through Embodied Imitation

“…If we think of the robots and the robot collective as the environment for behavioural evolution, noisy social learning is the principle mechanism by which behaviours can adapt to be better fitted to that environment. We agree with Alissandrakis et al [3] that variations might provide the evolutionary substrate for an artificial culture, and the work of this paper provides further exploration in this direction. In particular our experiments with different robot memory sizes have, we argue, provided new insights into how coherence or diversity in the population of behaviours in a collective is affected by behavioural memory size.…”

“…where N O and N C are the number of segments of the original path and its copy 3 . The overall quality of imitation, Q i , is a combination of 3 quality indicators:…”

“…Billard devised experiments in which robots were able to learn a proto-language by using imitation to match their environmental perceptions with the observed actions. Alissandrakis et al [3] developed the ALICE architecture to address the problems of imitation between agents with dissimilar embodiments.…”

Learning behavior fusion from demonstration

“…These approaches rely on the assumption that there exists a unique corresponding behavior underlying any one of the demonstrator's actions, which is a strong assumption that only holds in a limited number of situations. In contrast, research on the correspondence problem in imitation [17], [18] is addressing this assumption, by considering the variety of imitation behaviors that can result from different correspondence mappings. For example, even in simple navigation or object manipulation tasks, a human's actions are typically the result of combining multiple concurrent activities, such as seeking the goal, avoid static obstacles, avoid dynamic obstacles, keep equilibrium, etc.…”

Abstraction Levels for Robotic Imitation: Overview and Computational Approaches

“…The preference is translated in the corresponding utility function Q A , whose values are pre-programmed into the agent. 3 • The second source of information corresponds to the desire of the learner to replicate the effects observed in the demonstration. For example, the learner may wish to reproduce the change in the surroundings observed during the demonstration, or to replicate some particular transition experienced by the teacher.…”