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}.

Ask DALL·E 2, an image generation system created by OpenAI, to paint a picture of "goldfish slurping Coca-Cola on a beach," and it will spit out surreal images of exactly that. The program would have encountered images of beaches, goldfish, and Coca-Cola during training, but it's highly unlikely it would have seen one in which all three came together. Yet DALL·E 2 can assemble the concepts into something that might have made Dalí proud. Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences. DALL·E 2 is a type of generative model--a system that attempts to use training data to generate something new that's comparable to the data in terms of quality and variety.

Ask DALL·E 2, an image generation system created by OpenAI, to paint a picture of "goldfish slurping Coca-Cola on a beach," and it will spit out surreal images of exactly that. The program would have encountered images of beaches, goldfish and Coca-Cola during training, but it's highly unlikely it would have seen one in which all three came together. Yet DALL·E 2 can assemble the concepts into something that might have made Dalí proud. DALL·E 2 is a type of generative model -- a system that attempts to use training data to generate something new that's comparable to the data in terms of quality and variety. This is one of the hardest problems in machine learning, and getting to this point has been a difficult journey.