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From Agile Ground to Aerial Navigation: Learning from Learned Hallucination.
Zizhao
Wang, Xuesu Xiao, Alexander J Nettekoven, Kadhiravan Umasankar, Anika
Singh, Sriram Bommakanti, Ufuk Topcu, and Peter Stone.
In Proceedings
of the International Conference on Intelligent Robots and Systems (IROS 2021), October 2021.
1-minute
Video Summary; 15-minute Video Presentation
This paper presents a self-supervised Learning from Learned Hallucination (LfLH) method to learn fast and reactive motion planners for ground and aerial robots to navigate through highly constrained environments. The recent Learning from Hallucination (LfH) paradigm for autonomous navigation executes motion plans by random exploration in completely safe obstacle-free spaces, uses hand-crafted hallucination techniques to add imaginary obstacles to the robot’s perception, and then learns motion planners to navigate in realistic, highly-constrained, dangerous spaces. However, current handcrafted hallucination techniques need to be tailored for specific robot types (e.g., a differential drive ground vehicle), and use approximations heavily dependent on certain assumptions (e.g., a short planning horizon). In this work, instead of manually designing hallucination functions, LfLH learns to hallucinate obstacle configurations, where the motion plans from random exploration in open space are optimal, in a self-supervised manner. LfLH is robust to different robot types and does not make assumptions about the planning horizon. Evaluated in both simulated and physical environments with a ground and an aerial robot, LfLH outperforms or performs comparably to previous hallucination approaches, along with sampling- and optimization-based classical methods.
@InProceedings{iros21_lflh-wang,
author = {Zizhao Wang and Xuesu Xiao and Alexander J Nettekoven and Kadhiravan Umasankar and Anika Singh and Sriram Bommakanti and Ufuk Topcu and Peter Stone},
title = {From Agile Ground to Aerial Navigation: Learning from Learned Hallucination},
booktitle = {Proceedings of the International Conference on Intelligent Robots and Systems (IROS 2021)},
location = {Prague, Czech Republic},
month = {October},
year = {2021},
abstract = {
This paper presents a self-supervised Learning
from Learned Hallucination (LfLH) method to learn fast and
reactive motion planners for ground and aerial robots to
navigate through highly constrained environments. The recent
Learning from Hallucination (LfH) paradigm for autonomous
navigation executes motion plans by random exploration in
completely safe obstacle-free spaces, uses hand-crafted hallucination
techniques to add imaginary obstacles to the robot’s perception,
and then learns motion planners to navigate in realistic,
highly-constrained, dangerous spaces. However, current handcrafted
hallucination techniques need to be tailored for specific
robot types (e.g., a differential drive ground vehicle), and use
approximations heavily dependent on certain assumptions (e.g.,
a short planning horizon). In this work, instead of manually
designing hallucination functions, LfLH learns to hallucinate
obstacle configurations, where the motion plans from random
exploration in open space are optimal, in a self-supervised
manner. LfLH is robust to different robot types and does not
make assumptions about the planning horizon. Evaluated in
both simulated and physical environments with a ground and
an aerial robot, LfLH outperforms or performs comparably to
previous hallucination approaches, along with sampling- and
optimization-based classical methods.
},
wwwnote={<a href="https://youtu.be/X1X8fWTjW0E">1-minute Video Summary</a>;
<a href="https://youtu.be/BaqkZ1Tw9-M">15-minute Video Presentation</a>},
}
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