Tutor-Guided Interior Navigation With Deep Reinforcement Learning

Zeng, Fanyu; Wang, Chen; Ge, Shuzhi Sam

doi:10.1109/tcds.2020.3039859

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…Li et al [19] employed role-playing learning formulated with DRL for robotic navigation in a socially concomitant manner. Zeng et al [12] presented a tutor-student framework which consists of a "tutor" module and a "student" module. e "tutor" module can facilitate the training of the "student" module with its general features and knowledge.…”

Section: Related Workmentioning

confidence: 99%

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Fast Transfer Navigation for Autonomous Robots

Wang

Tao

et al. 2021

Journal of Robotics

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Navigation technology enables indoor robots to arrive at their destinations safely. Generally, the varieties of the interior environment contribute to the difficulty of robotic navigation and hurt their performance. This paper proposes a transfer navigation algorithm and improves its generalization by leveraging deep reinforcement learning and a self-attention module. To simulate the unfurnished indoor environment, we build the virtual indoor navigation (VIN) environment to compare our model and its competitors. In the VIN environment, our method outperforms other algorithms by adapting to an unseen indoor environment. The code of the proposed model and the virtual indoor navigation environment will be released.

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Section: Related Workmentioning

confidence: 99%

“…In our previous work [12], we proposed a tutor-student network for transfer navigation. Specifically, the tutor module must be pretrained in a maze to guide the student module navigate in an unseen maze.…”

Section: Introductionmentioning

confidence: 99%

Fast Transfer Navigation for Autonomous Robots

Wang

Tao

et al. 2021

Journal of Robotics

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Supervised Meta-Reinforcement Learning With Trajectory Optimization for Manipulation Tasks

Wang

Zhang

Zhu

et al. 2024

IEEE Trans. Cogn. Dev. Syst.

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Learning from small amounts of samples with reinforcement learning (RL) is challenging in many tasks, especially in real-world applications, such as robotics. Meta-Reinforcement Learning (meta-RL) has been proposed as an approach to address this problem by generalizing to new tasks through experience from previous similar tasks. However, these approaches generally perform meta-optimization by focusing direct policy search methods on validation samples from adapted policies, thus requiring large amounts of on-policy samples during meta-training. To this end, we propose a novel algorithm called Supervised Meta-Reinforcement Learning with Trajectory Optimization (SMRL-TO) by integrating Model-Agnostic Meta-Learning (MAML) and iLQR-based trajectory optimization. Our approach is designed to provide online supervision for validation samples through iLQRbased trajectory optimization and embed simple imitation learning into the meta-optimization rather than policy gradient steps. This is actually a bi-level optimization that needs to calculate several gradient updates in each meta-iteration, consisting of off-policy reinforcement learning in the inner loop and online imitation learning in the outer loop. SMRL-TO can achieve significant improvements in sample efficiency without humanprovided demonstrations, due to the effective supervision from iLQR-based trajectory optimization. In this paper, we describe how to use iLQR-based trajectory optimization to obtain labeled data and then how leverage them to assist the training of metalearner. Through a series of robotic manipulation tasks, we further show that compared with the previous methods, the proposed approach can substantially improve sample efficiency and achieve better asymptotic performance.

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