Efficient dynamic programming for high-dimensional, optimal motion planning by spectral learning of approximate value function symmetries

Proceedings - IEEE International Conference on Robotics and Automation

Volumn , Issue , 2011, Pages 6121-6127

  Vernaza, Paul ^a   Lee, Daniel D ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATE VALUE FUNCTION; COMPUTATIONAL EFFORT; COST STRUCTURE; DOMAIN-SPECIFIC KNOWLEDGE; HIGH QUALITY; HIGH-DIMENSIONAL; HIGH-DIMENSIONAL PROBLEMS; HOLONOMIC SYSTEM; MOTION PLANNERS; OPTIMAL CONTROL PROBLEM; OPTIMAL MOTION PLANNING; PLANNING PROBLEM; POST PROCESSING; ROBOT PLANNING; SAMPLING-BASED; SPECTRAL LEARNING; SPECTRAL METHODS; VALUE FUNCTIONS;

DYNAMIC PROGRAMMING; MOTION PLANNING; OPTIMAL CONTROL SYSTEMS;

ROBOTICS;

EID: 84871706001     PISSN: 10504729     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICRA.2011.5980552     Document Type: Conference Paper

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