Sim-Real Joint Reinforcement Transfer for 3D Indoor Navigation

Abstract

There has been an increasing interest in 3D indoor navigation, where a robot in an environment moves to a target according to an instruction. To deploy a robot for navigation in the physical world, lots of training data is required to learn an effective policy. It is quite labour intensive to obtain sufficient real environment data for training robots while synthetic data is much easier to construct by rendering. Though it is promising to utilize the synthetic environments to facilitate navigation training in the real world, real environment are heterogeneous from synthetic environment in two aspects. First, the visual representation of the two environments have significant variances. Second, the houseplans of these two environments are quite different. Therefore two types of information, i.e. visual representation and policy behavior, need to be adapted in the reinforcement model. The learning procedure of visual representation and that of policy behavior are presumably reciprocal. We propose to jointly adapt visual representation and policy behavior to leverage the mutual impacts of environment and policy. Specifically, our method employs an adversarial feature adaptation model for visual representation transfer and a policy mimic strategy for policy behavior imitation. Experiment shows that our method outperforms the baseline by 19.47% without any additional human annotations.

arXiv commentPaper has been accepted by CVPR 2019

References in corpus (13)

• Distilling the Knowledge in a Neural Network
• Playing Atari with Deep Reinforcement Learning
• VisDA: The Visual Domain Adaptation Challenge
• On Evaluation of Embodied Navigation Agents
• Unsupervised Cross-Domain Image Generation
• Matterport3D: Learning from RGB-D Data in Indoor Environments
• AI2-THOR: An Interactive 3D Environment for Visual AI
• Reinforcement Learning with Unsupervised Auxiliary Tasks
• Building Generalizable Agents with a Realistic and Rich 3D Environment
• Semi-supervised Feature Analysis by Mining Correlations among Multiple Tasks
• MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments
• Video-to-Video Synthesis
• CHALET: Cornell House Agent Learning Environment

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• DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesis
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