Cyan666/HandsOn-Checkpoints

HandsOn: paper multi-task checkpoints

The eight multi-task checkpoints selected in HandsOn: A Large-Scale Simulation Benchmark for Generalizable Dexterous Manipulation.

Upload complete. All eight model payloads were verified against the source files. See UPLOAD_VERIFIED.json and SHA256SUMS for per-file verification.

Paper · Code and inference adapters · Dataset · Model integration guide

Model	Optimizer step	Checkpoint path
π0.5	120,000	pi05/120000/
DM0.5	86,000	dm05/checkpoint-86000/
HY-Embodied-0.5-VLA	125,000	hy/checkpoint-125000/
LingBot-VLA	151,000	lingbot/checkpoint-151000/
OpenWAM	70,000	openwam/step-070000/mp_rank_00_model_states.pt
FastWAM	100,000	fastwam/step-100000/mp_rank_00_model_states.pt
Motus	100,000	motus/step-100000/mp_rank_00_model_states.pt
Spatial Forcing	90,000	spatial/90000/

These are fixed paper selections, not checkpoints chosen by benchmark success. The single-task DP reference and the prompted GPT-6 controller are outside this weight release.

Download

pip install -U huggingface_hub
# One model:
hf download Cyan666/HandsOn-Checkpoints --include 'pi05/**' CHECKPOINTS.json --local-dir ./handson-checkpoints
# All eight models:
hf download Cyan666/HandsOn-Checkpoints --local-dir ./handson-checkpoints

The native checkpoint formats and tensor bytes are preserved. Keep each Orbax params/ tree and each DCP state/ tree intact, including hidden metadata files. Standalone π0.5 optimizer state and OpenWAM RNG snapshots are omitted; native DCP and PyTorch containers can still contain training state. File sizes, model steps and relative paths are recorded in CHECKPOINTS.json.

Inference

Use the matching --model profile from the HandsOn model integration guide. These are native research checkpoints, not transformers.AutoModel packages. The release's adapters preserve checkpoint-specific normalization, image preprocessing, denoising and action horizons.

π0.5 normalization is at pi05/120000/assets/training_package_final18k.building/norm_stats.json; Spatial Forcing normalization is at spatial/90000/assets/v16_eef52/norm_stats.json. The other adapters use the normalization assets packaged in the code release.

Foundation-model weights, tokenizers, and FastWAM/Motus language caches are supplied separately as documented in the integration guide. Obtain foundation assets from their original publishers and retain their terms. The code provides a language-cache preparation command.

Example, after installing the π0.5 inference environment from the code repository:

python scripts/serve_paper_policy.py --model pi05 \
  --checkpoint /path/to/handson-checkpoints/pi05/120000 \
  --normalization /path/to/handson-checkpoints/pi05/120000/assets/training_package_final18k.building/norm_stats.json \
  --tokenizer /path/to/paligemma_tokenizer.model

All profiles output absolute physical EEF52 commands, ordered right arm/hand then left arm/hand, for the shared 30 Hz native evaluator. Loading a checkpoint alone does not reproduce the paper's evaluation protocol or certify a new rollout.

Provenance and terms

This repository publishes the selected HandsOn finetuning checkpoints in their original formats. Upstream model and component licenses continue to apply; this repository does not replace or broaden those terms. See the native source notices and the relevant foundation-model cards for component-specific information.

The upload verification records file sizes and SHA256 digests. It verifies storage and transfer integrity, not a fresh GPU evaluation or retraining.