| --- |
| tags: |
| - robotics |
| - kuavo |
| - imitation-learning |
| --- |
| |
| # 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: |
|
|
| ```text |
| 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](50_demos/HF_WATCHER.md) for save-completion and integrity gates. |
|
|
