Robo-DM Cuts Robot Dataset Storage by 70x and Decoding Time by 50x Without Accuracy Loss
Robo-DM is an open-source, cloud-based data management toolkit designed to address the storage, transfer, and training bottlenecks inherent in large-scale robot demonstration datasets. It uses EBML-based self-contained storage with both lossy and lossless compression, achieving up to 70x size reduction over the RLDS format used by Open X-Embodiment datasets. Sequential decoding speed is up to 50x faster than LeRobot via load-balanced, memory-mapped decoding caches. Critically, a 75x compressed dataset trained with Robo-DM on a pick-and-place task using In-Context Robot Transformer showed no degradation in downstream task accuracy.