University of Texas SysML Lab

SEAOTTER: Learned compression framework for cloud robotics combining autoencoder latents with JPEG compatibility

SEAOTTER is a compression framework for cloud robotics that pairs a sensor-embedded autoencoder with a one-time JPEG transcode step, enabling extreme compression ratios while remaining compatible with standard JPEG infrastructure. At 200:1 compression versus AVIF, the system achieves 7x faster encoding, 3.5x faster decoding, and +8% ImageNet top-1 accuracy. The approach targets the asymmetric power/bandwidth constraints of sensor, cloud, and consumer stages in robotic vision pipelines, and supports general-purpose and task-aware transcoding for dense and vision-language perception tasks.