Learning Behavioral Parameterization using Spatio-Temporal Case-Based Reasoning

This paper presents an approach to learning an optimal behavioral parameterization in the framework of a case-based reasoning methodology for autonomous navigation tasks. It is based on our previous work on a behavior-based robotic system that also employed spatio-temporal case-based reasoning in the selection of behavioral parameters but was not capable of learning new parameterizations. The present method extends the case-based reasoning module by making it capable of learning new and optimizing the existing cases where each case is a set of behavioral parameters. The learning process can either be a separate training process or be part of the mission execution. In either case, the robot learns an optimal parameterization of its behavior for different environments it encounters. The goal of this research is not only to automatically optimize the performance of the robot but also to avoid the manual configuration of behavioral parameters and the initial configuration of a case library, both of which require the user to possess good knowledge of robot behavior and the performance of numerous experiments. The presented method was integrated within a hybrid robot architecture and evaluated in extensive computer simulations, showing a significant increase in the performance over a nonadaptive system and a performance comparable to a non-learning CBR system that uses a hand-coded case library.

Authors

• Maxim Likhachev
• Michael Kaess
• Ronald C. Arkin

Keywords

• Libraries
• Robotics and automation
• Navigation
• Robot sensing systems
• Robotic assembly
• Orbital robotics
• Mobile robots
• Laboratories
• Educational institutions
• Optimization methods
• Case-based Reasoning
• Optimal Parameters
• Parameter Selection
• Robotic System
• Behavioral Parameters
• Configuration Parameters
• Navigation Task
• Robot Behavior
• Robot Performance
• Environmental Characteristics
• Spatial Features
• Random Selection
• Maximum Velocity
• Case Selection
• Current Environment
• Selection Step
• Output Parameters
• Performance In Cases
• Selection Stage
• Homogeneous Environment
• Temporal Similarity
• Spatial Similarity
• Temporal Vector
• Adaptive Step
• Matching Cases
• Velocity Of The Robot
• Angular Region
• Robot Trajectory
• Final Run