Sim-real joint reinforcement transfer for 3D indoor navigation

Zhu, Fengda1; Zhu, Linchao2; Yang, Yi2,3

10.1109/CVPR.2019.01165

2019

1063-6919

978-1-7281-3294-5

32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019

2019-June

11380-11389

15-20 June 2019

Long Beach, CA, United states

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE Computer Society

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 render-ing. 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. There-fore two types of information,i.e. visual representation and policy behavior, need to be adapted in the reinforce mentmodel. The learning procedure of visual representation and that of policy behavior are presumably reciprocal. We pro-pose 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 anda policy mimic strategy for policy behavior imitation. Experiment shows that our method outperforms the baseline by 19.47% without any additional human annotations.
© 2019 IEEE.

Deep Learning Representation Learning Robotics + Driving

第一

英语

EI ; CPCI-S

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods

WOS:000542649304101

20200508113552

Deep learning ; Reinforcement learning ; Indoor positioning systems ; Intelligent robots

Ergonomics and Human Factors Engineering:461.4 ; Artificial Intelligence:723.4 ; Robot Applications:731.6

EV Compendex

1.UTS-SUSTech Joint Research Centre, Southern University of Science and Technology, China
2.CAI, University of Technology Sydney, Australia
3.Baidu Research

Zhu, Fengda,Zhu, Linchao,Yang, Yi. Sim-real joint reinforcement transfer for 3D indoor navigation[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE Computer Society,2019:11380-11389.