NavRL++ improves sim-to-real transfer for RL navigation via empirical analysis of perturbations, perturbation-aware fine-tuning, and a Transformer temporal policy, with real-world validation showing outperformance over learning baselines and parity with optimization planners in static cases.
Towards Monocular Vision based Obstacle Avoidance through Deep Reinforcement Learning
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Obstacle avoidance is a fundamental requirement for autonomous robots which operate in, and interact with, the real world. When perception is limited to monocular vision avoiding collision becomes significantly more challenging due to the lack of 3D information. Conventional path planners for obstacle avoidance require tuning a number of parameters and do not have the ability to directly benefit from large datasets and continuous use. In this paper, a dueling architecture based deep double-Q network (D3QN) is proposed for obstacle avoidance, using only monocular RGB vision. Based on the dueling and double-Q mechanisms, D3QN can efficiently learn how to avoid obstacles in a simulator even with very noisy depth information predicted from RGB image. Extensive experiments show that D3QN enables twofold acceleration on learning compared with a normal deep Q network and the models trained solely in virtual environments can be directly transferred to real robots, generalizing well to various new environments with previously unseen dynamic objects.
verdicts
UNVERDICTED 3representative citing papers
A DRL method learns decentralized policies for multi-robot navigation from raw lidar in unknown environments via centralized training and decentralized execution.
Imitation learning pretraining of a ResNet-34 DDPG agent improves performance on image-based autonomous driving in simulation over pure IL or pure RL.
citing papers explorer
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NavRL++: A System-Level Framework for Improving Sim-to-Real Transfer in Reinforcement Learning-Based Robot Navigation
NavRL++ improves sim-to-real transfer for RL navigation via empirical analysis of perturbations, perturbation-aware fine-tuning, and a Transformer temporal policy, with real-world validation showing outperformance over learning baselines and parity with optimization planners in static cases.
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End-to-end Decentralized Multi-robot Navigation in Unknown Complex Environments via Deep Reinforcement Learning
A DRL method learns decentralized policies for multi-robot navigation from raw lidar in unknown environments via centralized training and decentralized execution.
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Improved Reinforcement Learning through Imitation Learning Pretraining Towards Image-based Autonomous Driving
Imitation learning pretraining of a ResNet-34 DDPG agent improves performance on image-based autonomous driving in simulation over pure IL or pure RL.