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We investigate a reinforcement approach for distributed sensing based on thelatent space derived from multi-modal deep generative models. Our contributionprovides insights to the following benefits: Detections can be exchangedeffectively between robots equipped with uni-modal sensors due to a sharedlatent representation of information that is trained by a Variational AutoEncoder (VAE). Sensor-fusion can be applied asynchronously due to thegenerative feature of the VAE. Deep Q-Networks (DQNs) are trained to minimizeuncertainty in latent space by coordinating robots to a Point-of-Interest (PoI)where their sensor modality can provide beneficial information about the PoI.Additionally, we show that the decrease in uncertainty can be defined as thedirect reward signal for training the DQN.

Coordinated Heterogeneous Distributed Perception based on Latent Space Representation