Viewing Robots as Primitive Learners

Project Description

The relationship between humanoid robotics and human-figure animation is synergistic, as each discipline provides tools and techniques of use to the other. Some of these have been mentioned previously. This work has demonstrated that fundamental motion exemplars for a robot can be generated using a gain-scheduled variant of the scattered data interpolation algorithms used for motion synthesis. Viewing robots as primitive learners, our results show that a robot can learn to interact purposefully with its environment through a developmental acquisition of sensory-motor coordination. Part of this effort has been to extend the Sensory Ego-Sphere (SES) for sensor fusion.

Additionally, we are employing manifold learning strategies to uncover the robot's underlying sensorimotor state structures and thus to develop better learning and control strategies. We have conducted several sets of experiments on Robonaut, NASA's humanoid robot, to validate our approach.


Students and Collaborators

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Bobby Bodenheimer