Abstract
A major challenge in robotics is the ability to learn, from novel experiences, new behavior that is useful for achieving new goals and skills. Autonomous systems must be able to learn solely through the environment, thus ruling out a priori task knowledge, tuning, extensive training, or other forms of pre-programming. Learning must also be cumulative and incremental, as complex skills are built on top of primitive skills. Additionally, it must be driven by intrinsic motivation because formative experience is gained through autonomous activity, even in the absence of extrinsic goals or tasks. This paper presents an approach to these issues through robotic implementations inspired by the learning behavior of human infants. We describe an approach to developmental learning and present results from a demonstration of longitudinal development on an iCub humanoid robot. The results cover the rapid emergence of staged behavior, the role of constraints in development, the effect of bootstrapping between stages, and the use of a schema memory of experiential fragments in learning new skills. The context is a longitudinal experiment in which the robot advanced from uncontrolled motor babbling to skilled hand/eye integrated reaching and basic manipulation of objects. This approach offers promise for further fast and effective sensory-motor learning techniques for robotic learning.
Highlights
The question of autonomy poses a hard challenge for robotics research—how can robots grow through the “openended acquisition of novel behavior?” That is, given an embodied robot system with some primitive actions, how can it learn appropriate new behaviors to deal with new and novel experiences
This paper has described a longitudinal experiment in robotic developmental learning
The result tables show the various learning times required for the robot to reach repeatable performance with reasonable accuracy and the total time for the whole process is less than 4 h. Such fast learning rates are crucial in real robot systems where online learning is essential, and training through many thousands of action cycles is quite impossible. This performance, which is typical of all our experiments, show that developmental learning algorithms offer serious potential for future real-time autonomous robots that must cope with novel events
Summary
The question of autonomy poses a hard challenge for robotics research—how can robots grow through the “openended acquisition of novel behavior?” That is, given an embodied robot system with some primitive actions, how can it learn appropriate new behaviors to deal with new and novel experiences. We report on experiments that illustrate the value of a developmental attack on this issue. Developmental robotics is a recent field of study that recognizes the role of epigenetic development as a new paradigm for adaptation and learning in robotics. Most research in this field reports on specific topics in development such as motivation, embodiment, enactive growth, imitation, self-awareness, agent interaction and other issues. Such investigations are exploring effective modeling methods and increasing our understanding of the many and varied aspects of the phenomenon of development. For general principles and reviews see Lungarella et al (2003); Asada et al (2009); Stoytchev (2009)
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