RwH Kuavo checkpoints
Kuavo 5W policies trained on the private RwH-Lab/kuavo-dataset-skill dataset. This repository receives saved checkpoints as training progresses; the presence of an early checkpoint does not imply the full 20k run has finished.
50 demos
Four policies: SmolVLA (smolvla), MultiTask-DiT (multi_task_dit),
Pi0.5 (pi05), GR00T N1.5 (gr00t). Each policy publishes four checkpoints:
50_demos/<policy>/checkpoints/
checkpoint-5K/
checkpoint-10K/
checkpoint-15K/
checkpoint-20K/
Every published directory contains pretrained_model/, checkpoint_manifest.json,
a loading note and provenance. Manifests record the source step and each payload
file's size and SHA256. Publishing verifies remote paths and content hashes.
Optimizer/scheduler/RNG state is retained on the training server.
SmolVLA and Pi0.5 are LoRA adapters, not standalone base weights. Their original HF base model IDs and pinned revisions are recorded in provenance/model_assets.json. GR00T and DiT contain full policy weights. Original upstream access requirements apply.
Training contract: 50 original episodes, 7 tasks, 30→10Hz, current state20D, action23D (future actual torso position4D, recorded arm/gripper commands16D, recorded base velocity commands3D), global batch64 per policy, seed42, 20k updates, no validation split. Samples do not cross task boundaries.
Use both saved processors and their normalization statistics when loading a checkpoint. GR00T needs the included compatibility patch to preserve per-sample task text; GR00T and DiT need aspect-preserving224×224 letterbox preprocessing. Follow each checkpoint's provenance/CONTRACT.md and compat.py. Use runtime_versions.json to identify the exact LeTools/LeRobot commits and package versions.
No simulator or real-robot task success is claimed by offline training checks. See watcher design for save-completion and integrity gates.