Coordinated Heterogeneous Distributed Perception based on Latent Space Representation
This addresses the challenge of heterogeneous sensor coordination in robotics, but it appears incremental as it combines existing methods like VAEs and DQNs for a specific application.
The paper tackles the problem of coordinating robots with different sensors for distributed perception by using a shared latent space from a VAE to exchange detections and apply sensor fusion asynchronously, and trains DQNs to minimize uncertainty by guiding robots to points of interest.
We investigate a reinforcement approach for distributed sensing based on the latent space derived from multi-modal deep generative models. Our contribution provides insights to the following benefits: Detections can be exchanged effectively between robots equipped with uni-modal sensors due to a shared latent representation of information that is trained by a Variational Auto Encoder (VAE). Sensor-fusion can be applied asynchronously due to the generative feature of the VAE. Deep Q-Networks (DQNs) are trained to minimize uncertainty 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 the direct reward signal for training the DQN.