Published February 27, 2019 | Version v1
Conference paper Restricted

Practical Resolution Methods for MDPs in Robotics Exemplified With Disassembly Planning


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 hierarchies of subtasks and solve smaller, simplified problems. We propose a decision-making unit that operates in a probabilistic planning setting through stochastic shortest path problems, which generalize the most common types of MDPs. Our decision-making unit combines: first, automatic hierarchical organization of subtasks; and second, on-line resolution via determinization. We argue that several applications of planning benefit from these two strategies. We exemplify our approach with a robotized disassembly application. The disassembly problem is modeled in probabilistic planning definition language, and serves to define our experiments. Our results show many advantages of our method over LRTDP, such as a better capability to handle problems with large state spaces and state definitions that change when new fluents are discovered.



The record is publicly accessible, but files are restricted to users with access.

Request access

If you would like to request access to these files, please fill out the form below.

You need to satisfy these conditions in order for this request to be accepted:

RA-Letters are Hybrid Open Access: papers are free of charge to authors, and are freely available to IEEE RAS members.

You are currently not logged in. Do you have an account? Log in here

Additional details


IMAGINE – Robots Understanding Their Actions by Imagining Their Effects 731761
European Commission