Learning about Objects through Action - Initial Steps towards Artificial Cognition
Abstract
Within the field of Neuro Robotics we are driven primarily by the desire to understand how humans and animals live and grow and solve everyday problems. To this aim we adopted a "learn by doing" approach by building artificial systems, e.g. robots, that not only look like human beings but also represent a model of some brain process. They should, ideally, behave and interact like human beings (being situated). The main emphasis in robotics has been on systems that act as a reaction to an external stimulus (e.g. tracking, reaching), rather than as a result of an internal drive to explore or "understand" the environment. We think it is now appropriate to try to move from acting, in the sense explained above, to "understanding." As a starting point we addressed the problem of learning about the effects and consequences of self-generated actions. How does the robot learn how to pull an object toward itself or to push it away? How does the robot learn that spherical objects roll while a cube only slides if pushed? Interacting with objects is important because it implicitly explores object representation, even understanding, and can provide definition of objecthood that could not be grasped with a mere passive observation of the world. Further, learning to understand what one's own body can do is an essential step toward learning by imitation. In this view two actions are similar not only if their kinematics and dynamics are similar but rather if the effects on the external world are the same.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2001
- Accession Number
- ADA434778
Entities
People
- Giorgio Metta
- Giulio Sandini
- Lorenzo Natale
- Paul M. Fitzpatrick
- Sajit Rao
Organizations
- Massachusetts Institute of Technology