Record, train, and deploy from one place with Strands Agents, LeRobot, and Hugging Face Storage Buckets

Summary

This walkthrough presents a streaming data loop for Strands Robots that records robot demonstrations, stores them in Hugging Face Storage Buckets, trains from the growing dataset, and deploys a resulting policy back to hardware. It uses the LeRobot on-disk format throughout: Robot("so100") records through DatasetRecorder, sync_dataset_to_bucket writes changed bytes to a mutable bucket, and stream_dataset reads the dataset from the Hub without a full local download while decoding camera video on the fly. The agent can decide which episodes to keep and when to retrain, while the same Robot object records and streams data; deployment changes the robot mode keyword from simulation to real. The default simulation path runs with a mock policy that produces a valid dataset but not a useful one, while hardware, bucket, and training paths require additional dependencies.

Takeaways

1. 1

   Storage Buckets provide mutable, non-versioned, Xet-backed storage for the working data layer, while versioned dataset repositories retain revisions and commits.

2. 2

   stream_dataset reads LeRobot data over the Hub without a full local copy and decodes camera video on the fly; the dataset remains in LeRobot format throughout.

3. 3

   The default simulation path uses a mock policy that can produce a valid dataset but not a useful policy; training requires the separate lerobot[training] installation.