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Impact • A methodology for on-line human-robot control

Skill Learning by Primitives-Based Demonstration & Imitation. New Ideas • Low cost, lightweight, wireless, real-time human motion collection • Cross-kinematic analysis for learning motion primitives from various sources • Creating controllers from collected sensory data

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Impact • A methodology for on-line human-robot control

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  1. Skill Learning by Primitives-Based Demonstration & Imitation New Ideas • Low cost, lightweight, wireless, real-time human motion collection • Cross-kinematic analysis for learning motion primitives from various sources • Creating controllers from collected sensory data • Randomized dynamic roadmaps for humanoid reaching and for planning primitive motion sequencing Motion controllers based on randomized roadmaps and collected sensory/motion data USC motion suit A Cross-Kinematics Metric for Imitation Learning Robonaut Control Accomplishments Impact • A methodology for on-line human-robot control • Parameterized controllers allow human-robot cooperation for a variety of tasks • Increased autonomy for humanoid robots Teleoperation of NASA Robonaut Integrated demo in Robosim Sensory data analysis USC motion suit Year 1 Year 2 Cross-Kinematic Metric Reaching Motion Planning USC humanoid/Robonaut test-beds Motion analysis from Robonaut Data PI: M. Matarić, USC

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