𝔖 Bobbio Scriptorium
✦   LIBER   ✦

A behavior-based mobile robot architecture for Learning from Demonstration

✍ Scribed by Michael Kasper; Gernot Fricke; Katja Steuernagel; Ewald von Puttkamer

Publisher
Elsevier Science
Year
2001
Tongue
English
Weight
609 KB
Volume
34
Category
Article
ISSN
0921-8890

No coin nor oath required. For personal study only.

✦ Synopsis

Autonomous mobile robots (AMRs), to be truly flexible, should be equipped with learning capabilities, which allow them to adapt effectively to a dynamic and changing environment. This paper proposes a modular, behavior-based control architecture, which is particularly suited for "Learning from Demonstration" experiments in the spatial domain. The robot learns sensory-motor behaviors online by observing the actions of a person, another robot or another behavior. Offline learning phases are not necessary but might be used to trim the attained representation. First results applying RBF-approximation, growing neural cell structures and probabilistic models for progress estimation, are presented.

πŸ“œ SIMILAR VOLUMES

A neuro-fuzzy system architecture for be
A neuro-fuzzy system architecture for behavior-based control of a mobile robot in unknown environments
✍ Wei Li; Chenyu Ma; F.M. Wahl πŸ“‚ Article πŸ“… 1997 πŸ› Elsevier Science 🌐 English βš– 522 KB

A neuro-fuzzy system architecture for behavior-based control of a mobile robot in unknown environments is presented. A neural network is used to understand environments. Its inputs are a heading angle between the robot and a specified target, and range information acquired by an array of ultrasonic

A framework for defining and learning fu
A framework for defining and learning fuzzy behaviors for autonomous mobile robots
✍ Humberto MartΓ­nez BarberΓ‘; Antonio GΓ³mez Skarmeta πŸ“‚ Article πŸ“… 2002 πŸ› John Wiley and Sons 🌐 English βš– 144 KB

In this paper we show our work on the use of fuzzy behaviors in the field of autonomous mobile robots. We address here how we use learning techniques to efficiently coordinate the conflicts between the different behaviors that compete with each other to take control of the robot. We use fuzzy rules

Behavior generation for a mobile robot b
Behavior generation for a mobile robot based on the adaptive fitness function
✍ Eiji Uchibe; Masakazu Yanase; Minoru Asada πŸ“‚ Article πŸ“… 2002 πŸ› Elsevier Science 🌐 English βš– 258 KB

We have to prepare the evaluation (fitness) function to evaluate the performance of the robot when we apply the machine learning techniques to the robot application. In many cases, the fitness function is composed of several aspects. Simple implementation to cope with the multiple fitness functions