Learning Similar Tasks From Observation and Practice

This paper presents a case study of learning to select behavioral primitives and generate subgoals from observation and practice. Our approach uses local features to generalize across tasks and global features to learn from practice. We demonstrate this approach applied to the marble maze task. Our robot uses local features to initially learn primitive selection and subgoal generation policies from observing a teacher maneuver a marble through a maze. The robot then uses this information as it tries to traverse another maze, and refines the information during learning from practice

Authors

• Darrin C. Bentivegna
• Christopher G. Atkeson
• Gordon Cheng

Keywords

• Intelligent robots
• Orbital robotics
• Humanoid robots
• Educational robots
• Hardware
• Libraries
• USA Councils
• Laboratories
• Education
• Navigation
• Similar Tasks
• Local Features
• Global Features
• Learning Rate
• Scaling Factor
• Local Information
• Feature Learning
• Kernel Function
• Number Of Data Points
• Global Information
• Informal Learning
• Q-learning
• Global Representation
• Q-function
• Angle Velocity
• Kernel Regression
• Query Point
• End Location
• Roll Off