University of Texas researchers have developed SEAOTTER, a compression framework for cloud robotics that combines a sensor-embedded autoencoder with a learned JPEG transcode. SEAOTTER achieves significantly faster encoding and decoding than AVIF at high compression ratios, while also improving downstream computer vision accuracy and maintaining compatibility with existing JPEG infrastructure. The system leverages AI to optimize color transforms and quantization matrices for specific applications, producing standard JPEG files that can be efficiently decoded.