Keywords: big robot data, data management
TL;DR: Robo-DM is a cloud-based toolkit that optimizes robot data storage and loading using EBML/MKV, achieving up to 70x compression and 50x faster loading with minimal impact on training performance.
Abstract: Recent work suggests that very large datasets of teleoperated robot demonstrations can train transformer-based models that have the potential to generalize to new scenes, robots, and tasks. However, curating, distributing, and loading large datasets of robot trajectories, which typically consist of video, textual, and numerical modalities—including streams from multiple cameras—remains challenging. We propose Robo-DM, an efficient cloud-based data management toolkit for collecting, sharing, and learning with robot data. With Robo-DM, robot datasets are stored in a self-contained format with Extensible Binary Meta Language (EBML). Robo-DM reduces the size of robot trajectory data, transfer costs, and data load time during training. In particular, compared to the RLDS format used in OXE datasets, Robo-DM’s compression saves space by up to 70x (lossy) and 3.5x (lossless). Robo-DM also accelerates data retrieval by load-balancing video decoding with memory-mapped decoding caches. Compared to LeRobot, a framework that also uses lossy video compression, Robo-DM is up to 50x faster. In fine-tuning Octo, a transformer-based robot policy with 73k episodes with RT-1 data, Robo-DM does not incur any loss in training performance. We physically evaluate a model trained by Robo-DM with lossy compression, a pick-and-place task, and In-Context Robot Transformer. Robo-DM uses 75x compression of the original dataset and does not suffer any reduction in downstream task accuracy.
Previous Publication: No
Submission Number: 19
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