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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xu Chu Ding Jing Wang Lahijanian, M. Paschalidis, I.C. Belta, C.A. |
| Copyright Year | 2012 |
| Description | Author affiliation: Division of System Eng., Boston University, MA 02215, USA (Jing Wang; Lahijanian, M.; Paschalidis, I.C.; Belta, C.A.) || Embedded Systems and Networks group, United Technologies Research Center, East Hartford, CT 06108, USA (Xu Chu Ding) |
| Abstract | In this paper, we consider the problem of deploying a robot from a specification given as a temporal logic statement about some properties satisfied by the regions of a large, partitioned environment. We assume that the robot has noisy sensors and actuators and model its motion through the regions of the environment as a Markov Decision Process (MDP). The robot control problem becomes finding the control policy maximizing the probability of satisfying the temporal logic task on the MDP. For a large environment, obtaining transition probabilities for each state-action pair, as well as solving the necessary optimization problem for the optimal policy are usually not computationally feasible. To address these issues, we propose an approximate dynamic programming framework based on a least-square temporal difference learning method of the actor-critic type. This framework operates on sample paths of the robot and optimizes a randomized control policy with respect to a small set of parameters. The transition probabilities are obtained only when needed. Hardware-in-the-loop simulations confirm that convergence of the parameters translates to an approximately optimal policy. |
| Starting Page | 4687 |
| Ending Page | 4692 |
| File Size | 1321094 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781467314039 |
| ISSN | 10504729 |
| e-ISBN | 9781467314053 |
| e-ISBN | 9781467314046 |
| DOI | 10.1109/ICRA.2012.6225290 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-05-14 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Computational modeling Robot sensing systems Materials requirements planning Manganese Automata Markov processes |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software |
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