Memory-Based Robot Learning

We present a memory-based local modeling approach to robot learning using a nonparametric regression technique, locally weighted regression. The model of the task to be performed is represented by infinitely many local linear models, the (hyper-) tangent planes at every query point. This is in contrast to other methods using finite set of linear models to accomplish a piecewise linear model. Architectural parameters of our approach, such as distance metrics, are a function of the current query point instead of being global. Statistical tests are presented for when a local model is good enough such that it can be reliably used to build a local controller. These statistical measures also direct the exploration of the robot. We explicitly deal with the case where prediction accuracy requirements exist during exploration: by gradually shifting a center of exploration and controlling the speed of the shift with local prediction accuracy, a goal-directed exploration of state space takes place along the fringes of the current data support until the task goal is achieved. We illustrate this approach by describing how it has been used to enable a robot to learn a juggling task. >

Authors

• Stefan Schaal
• Christopher G. Atkeson

Keywords

• Orbital robotics
• State-space methods
• Learning systems
• Cognitive robotics
• Accuracy
• Artificial intelligence
• Laboratories
• Brain modeling
• Intelligent robots
• Testing
• Active Control
• Optimal Control
• State Space
• Fitting Parameters
• Learning System
• Regression Parameters
• Forward Model
• Task Model
• Inverse Model
• Prediction Intervals
• Cross-validation Error
• Learning Control
• Query Point
• Local Linear
• Instance-based Learning
• Half Of The Plots