Autonomous Learning of Tool Affordances by a Robot

The ability to use tools is one of the hallmarks of intelligence. Tool use is fundamental to human life and has been for at least the last two million years. We use tools to extend our reach, to amplify our physical strength, to transfer objects and liquids, and to achieve many other everyday tasks. A large number of animals have also been observed to use tools (Beck 1980). Some birds, for example, use twigs or cactus pines to probe for larvae in crevices which they cannot reach with their beaks. Sea otters use stones to open hard-shelled mussels. Chimpanzees use stones to crack nuts open and sticks to reach food, dig holes, or attack predators. These examples suggest that the ability to use tools is an adaptation mechanism used by many organisms to overcome the limitations imposed on them by their anatomy. Despite the widespread use of tools in the animal world, however, studies of autonomous robotic tool use are still rare. This abstract describes the empirical evaluation of one specific way of representing and learning the functional properties or affordances (Gibson 1979) of tools. A longer version of this paper appears in (Stoytchev 2005).