Where do Actions Come From? Autonomous Robot Learning of Objects and Actions

Joseph Modayil, Benjamin Kuipers

Decades of AI research have yielded techniques for learning, inference, and planning that depend on human-provided ontologies of self, space, time, objects, actions, and properties. Since robots are constructed with low-level sensor and motor interfaces that do not provide these concepts, the human robotics researcher must create the bindings between the required high-level concepts and the available low-level interfaces. This raises the developmental learning problem for robots of how a learning agent can create high-level concepts from its own low-level experience. Prior work has shown how objects can be individuated from low-level sensation, and certain properties can be learned for individual objects. This work shows how high-level actions can be learned autonomously by searching for control laws that reliably change these properties in predictable ways. We present a robust and efficient algorithm that creates reliable control laws for perceived objects. We demonstrate on a physical robot how these high-level actions can be learned from the robot's own experiences, and can then applied to a learned object to achieve a desired goal.

Subjects: 15.3 Control; 17. Robotics

Submitted: Jan 24, 2007

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