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Deep Imitative Reinforcement Learning for Temporal Logic Robot Motion Planning with Noisy Semantic Observations

Publication ,  Conference
Gao, Q; Pajic, M; Zavlanos, MM
Published in: Proceedings - IEEE International Conference on Robotics and Automation
May 1, 2020

In this paper, we propose a Deep Imitative Q-learning (DIQL) method to synthesize control policies for mobile robots that need to satisfy Linear Temporal Logic (LTL) specifications using noisy semantic observations of their surroundings. The robot sensing error is modeled using probabilistic labels defined over the states of a Labeled Transition System (LTS) and the robot mobility is modeled using a Labeled Markov Decision Process (LMDP) with unknown transition probabilities. We use existing product-based model checkers (PMCs) as experts to guide the Q-learning algorithm to convergence. To the best of our knowledge, this is the first approach that models noise in semantic observations using probabilistic labeling functions and employs existing model checkers to provide suboptimal instructions to the Q-learning agent.

Duke Scholars

Published In

Proceedings - IEEE International Conference on Robotics and Automation

DOI

ISSN

1050-4729

ISBN

9781728173955

Publication Date

May 1, 2020

Start / End Page

8490 / 8496
 

Citation

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Gao, Q., Pajic, M., & Zavlanos, M. M. (2020). Deep Imitative Reinforcement Learning for Temporal Logic Robot Motion Planning with Noisy Semantic Observations. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 8490–8496). https://doi.org/10.1109/ICRA40945.2020.9197297
Gao, Q., M. Pajic, and M. M. Zavlanos. “Deep Imitative Reinforcement Learning for Temporal Logic Robot Motion Planning with Noisy Semantic Observations.” In Proceedings - IEEE International Conference on Robotics and Automation, 8490–96, 2020. https://doi.org/10.1109/ICRA40945.2020.9197297.
Gao Q, Pajic M, Zavlanos MM. Deep Imitative Reinforcement Learning for Temporal Logic Robot Motion Planning with Noisy Semantic Observations. In: Proceedings - IEEE International Conference on Robotics and Automation. 2020. p. 8490–6.
Gao, Q., et al. “Deep Imitative Reinforcement Learning for Temporal Logic Robot Motion Planning with Noisy Semantic Observations.” Proceedings - IEEE International Conference on Robotics and Automation, 2020, pp. 8490–96. Scopus, doi:10.1109/ICRA40945.2020.9197297.
Gao Q, Pajic M, Zavlanos MM. Deep Imitative Reinforcement Learning for Temporal Logic Robot Motion Planning with Noisy Semantic Observations. Proceedings - IEEE International Conference on Robotics and Automation. 2020. p. 8490–8496.

Published In

Proceedings - IEEE International Conference on Robotics and Automation

DOI

ISSN

1050-4729

ISBN

9781728173955

Publication Date

May 1, 2020

Start / End Page

8490 / 8496