Learning hierarchical partially observable Markov decision process models for robot navigation

Proceedings - IEEE International Conference on Robotics and Automation

Volumn 1, Issue , 2001, Pages 511-516

  Theocharous, G ^a   Rohanimanesh, K ^a   Mahadevan, S ^a  

^a Michigan State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; HIERARCHICAL SYSTEMS; MARKOV PROCESSES; NAVIGATION; ROBOT LEARNING;

ROBOT NAVIGATION;

ROBOTICS;

EID: 0034860462     PISSN: 10504729     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (8)

1. A robot navigation architecture based on partially observable Markov decision process models
2. Dervish: An office-navigation robot
3. Tutorial on hidden Markov models and selected applications in speech recognition
4. Learning topological maps with weak local odeometric information
5. The hierarchical hidden Markov model: Analysis and applications
6. Planning and acting in partially observable stochastic domains
7. Acting under uncertainty: Discrete bayesian models for mobile robot navigation
8. Fast concept learning for mobile robots