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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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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