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Machine Learning-Based Robotics in Unstructured Environments
Frost & Sullivan, Dec 2006, Pages: 79
1. Executive Summary
- 1. Scope & Methodology
-- 1. Scope of the Research Service
-- 2. Methodology
- 2. Key Research Findings
-- 1. Technology Snapshot
-- 2. Noteworthy Emerging Technologies and Applications
-- 3. Analyst Insights
2. Technology; Applications--Viewpoint and Roadmap
- 1. Technology Primer
-- 1. The Essential Components of Machine Learning
-- 2. Common Learning Problems
-- 3. Key Types of Machine Learning
-- 4. What Robots Should Learn ?
-- 5. Robot Learning--A Difficult Machine Learning Problem
-- 6. Sensor Technology for Machine Learning-Based Robots
-- 7. Significance of Feature Identification for Navigation in Unstructured Environments
-- 8. The Different Sensors To Be Utilized
-- 9. Hardware Issues in the Design of Machine Learning-Based Robots
-- 10. Paradigms in Robot Learning
-- 11. Technology Roadmap of Machine Learning-Based Robotics
- 2. Applications Viewpoint
-- 1. Service Robotics
-- 2. Networked Robotics
- 3. Applications Roadmap
-- 1. Service Robotics
-- 2. Networked Robotics
3. Technology Adoption Factor Analysis
- 1. Service Robotics
-- 1. Technology Drivers
-- 2. Technology Challenges
- 2. Networked Robotics
-- 1. Technology Drivers
-- 2. Technology Challenges
4. Assessment of Global Research and Innovations
- 1. Research Work at Universities
-- 1. Standford AI Robot (STAIR) Project
-- 2. Cognitive Agent that Learns and Observes (CALO) Project
-- 3. Robotic Soccer: The Brainstormers--University of Osnabruck; Germany
-- 4. Intelligent Robot Systems for Elimination Units for Marine Oil Pollution (EU-MOP) Project--Greece
-- 5. Cognitive Systems for Cognitive Assistants (CoSy)--Germany
-- 6. Swarm Robotics; Universite Libre de Bruxelles--Belgium
-- 7. Home Environment Cleaning Thoroughly Operating Robot (HECTOR) Project--Germany
-- 8. Human Observation-Based Motion Control Strategies in Intelligent Space--Japan
-- 9. Multimodal Teleoperation Interface for a Mobile Robot Based on Ubiquitous Information Access--Japan
-- 10. Toward a Touching Presence--Technische Universitat Munchen; Germany
- 2. Work at Corporate Establishments
-- 1. Learning Applied to Ground Robots (LAGR)--USA
-- 2. Autonomous Navigation Technology--Switzerland
-- 3. Corporate Contributions
-- 4. International Comparisons
5. Directory of Patents and Key Contacts
- 1. Key Patents
-- 1. List of Key Patents--2006
-- 2. List of Key Patents--2005
- 2. Contacts
-- 1. Universities
-- 2. Corporates
6. Decision Support Database
- 1. Decision Support Database Tables
-- 1. Number of Manufacturing Units--(1999-2006)
-- 2. Number of Service Organizations--(1999-2006)
-- 3. Number of Households--(1999-2006)
-- 4. Labor Force Population--(1999-2006)
-- 5. Number of Persons Employed in Manufacturing Industry--(1999-2006)
-- 6. Number of Persons Employed in Service Industry--(1999-2006)
List of Figures
Chapter 2
Supervised learning
Unsupervised learning
Reinforcement learning
Traditional decomposition of an intelligent control system
New approach to an intelligent control system
Application of networked robotics
Roadmap to personal robots
Network robotics--An approximate timeline
Chapter 3
Technology challenge roadmap--Components
Technology challenge roadmap--Advanced behaviors
Network robotics research challenges
Technical challenges faced in network robots
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