The iCub Platform: A Tool for Studying Intrinsically Motivated Learning
Intrinsically motivated robots are machines designed to operate for long periods of time, performing tasks for which they have not been programmed. These robots make extensive use of explorative, often unstructured actions in search for opportunities to l
- PDF / 935,523 Bytes
- 26 Pages / 439.36 x 666.15 pts Page_size
- 20 Downloads / 197 Views
Abstract Intrinsically motivated robots are machines designed to operate for long periods of time, performing tasks for which they have not been programmed. These robots make extensive use of explorative, often unstructured actions in search of opportunities to learn and extract information from the environment. Research in this field faces challenges that need advances not only on the algorithms but also on the experimental platforms. The iCub is a humanoid platform that was designed to support research in cognitive systems. We review in this chapter the chief characteristics of the iCub robot, devoting particular attention to those aspects that make the platform particularly suitable to the study of intrinsically motivated learning. We provide details on the software architecture, the mechanical design, and the sensory system. We report examples of experiments and software modules to show how the robot can be programmed to obtain complex behaviors involving interaction with the environment. The goal of this chapter is to illustrate the potential impact of the iCub on the scientific community at large and, in particular, on the field of intrinsically motivated learning.
1 Introduction Developmental robotics is a young field of research that attempts to build artificial systems with cognitive abilities (see Lungarella et al. 2003 for a review). In contrast to other, more traditional, approaches, researchers in this field subscribe
L. Natale () F. Nori G. Metta M. Fumagalli S. Ivaldi U. Pattacini M. Randazzo A. Schmitz G. Sandini Department of Robotics, Brain and Cognitive Sciences, Istituto Italiano di Tecnologia, Genova, Italy e-mail: [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected] G. Baldassarre and M. Mirolli (eds.), Intrinsically Motivated Learning in Natural and Artificial Systems, DOI 10.1007/978-3-642-32375-1 17, © Springer-Verlag Berlin Heidelberg 2013
433
434
L. Natale et al.
to the hypothesis that cognition is not hard coded but that, on the contrary, it emerges autonomously from the physical interaction between the agent and the environment (Weng et al. 2000; Zlatev and Balkenius 2001). Developmental robotics is a strongly interdisciplinary field that brings together researchers from behavior and brain sciences (psychology, neuroscience), engineering (robotics, computer science), and artificial intelligence, motivated by the conviction that each field has a lot to learn from the others. Roboticists in particular have realized that the real world is too complex to be modeled and too dynamic to hope that static models are of any use. For this reason, they have started to seek inspiration from biological systems and how they deal with the complexity of the world in which they live. The study of humans and biological systems confirms that nature solved this problem by designing systems that undergo a constant p
Data Loading...
