Learning From Observation Using Primitives

This paper describes the rise of task primitives in robot learning from observation. A framework is developed that uses observed data to initially learn a task and the agent then goes on to increase its performance through repeated task performance (learning from practice). Data that is collected while the human performs a task is parsed into small parts of the task called primitives. Modules are created for each primitive that encode the movements required during the performance of the primitive, and when and where the primitives are performed. The feasibility of this method is currently being tested with agents that learn to play a virtual and an actual air hockey game.

Authors

• Darrin C. Bentivegna
• Christopher G. Atkeson

Keywords

• Humans
• Hardware
• Robot vision systems
• Cameras
• Testing
• Accelerated aging
• Life estimation
• Performance evaluation
• Acceleration
• Usability
• Robot Learning
• Environmental Conditions
• Collision
• Cognitive Domains
• Actuator
• Visual System
• Kernel Function
• Hardware Implementation
• Generation Module
• Humanoid Robot
• Kernel Regression
• Query Point
• Goal Area