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Practical Resolution Methods for MDPs in Robotics Exemplified With Disassembly Planning

Suárez-Hernández, Alejandro; Torras, Carme; Alenyà, Guillem;
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Abstract
In this letter, we focus on finding practical resolution methods for Markov decision processes (MDPs) in robotics. Some of the main difficulties of applying MDPs to real-world robotics problems are: first, having to deal with huge state spaces; and second, designing a method that is robust enough to dead ends. These complications restrict or make more difficult the application of methods, such as value iteration, policy iteration, or labeled real-time dynamic programming (LRTDP). We see in determinization and heuristic search a way to successfully work around these problems. In addition, we believe that many practical use cases offer the opportunity to identify ...
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Funded by
EC| IMAGINE
Project
IMAGINE
Robots Understanding Their Actions by Imagining Their Effects
  • Funder: European Commission (EC)
  • Project Code: 731761
  • Funding stream: H2020
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