Zero-Shot Wireless Indoor Navigation through Physics-Informed
Reinforcement Learning
- URL: http://arxiv.org/abs/2306.06766v2
- Date: Fri, 15 Sep 2023 20:19:46 GMT
- Title: Zero-Shot Wireless Indoor Navigation through Physics-Informed
Reinforcement Learning
- Authors: Mingsheng Yin, Tao Li, Haozhe Lei, Yaqi Hu, Sundeep Rangan, and
Quanyan Zhu
- Abstract summary: This work proposes a novel physics-informed RL (PIRL) for indoor robot navigation using wireless signals.
After learning to utilize the physics information, the agent can transfer this knowledge across different tasks and navigate in an unknown environment without fine-tuning.
It is shown that the PIRL significantly outperforms both e2e RL and RL-based solutions in terms of generalization and performance.
- Score: 21.716538715570756
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The growing focus on indoor robot navigation utilizing wireless signals has
stemmed from the capability of these signals to capture high-resolution angular
and temporal measurements. Prior heuristic-based methods, based on radio
frequency propagation, are intuitive and generalizable across simple scenarios,
yet fail to navigate in complex environments. On the other hand, end-to-end
(e2e) deep reinforcement learning (RL), powered by advanced computing
machinery, can explore the entire state space, delivering surprising
performance when facing complex wireless environments. However, the price to
pay is the astronomical amount of training samples, and the resulting policy,
without fine-tuning (zero-shot), is unable to navigate efficiently in new
scenarios unseen in the training phase. To equip the navigation agent with
sample-efficient learning and {zero-shot} generalization, this work proposes a
novel physics-informed RL (PIRL) where a distance-to-target-based cost
(standard in e2e) is augmented with physics-informed reward shaping. The key
intuition is that wireless environments vary, but physics laws persist. After
learning to utilize the physics information, the agent can transfer this
knowledge across different tasks and navigate in an unknown environment without
fine-tuning. The proposed PIRL is evaluated using a wireless digital twin (WDT)
built upon simulations of a large class of indoor environments from the AI
Habitat dataset augmented with electromagnetic (EM) radiation simulation for
wireless signals. It is shown that the PIRL significantly outperforms both e2e
RL and heuristic-based solutions in terms of generalization and performance.
Source code is available at \url{https://github.com/Panshark/PIRL-WIN}.
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