ACT fine-tuned on HARP VLA train37

This is the validated final LeRobot policy checkpoint from the joint 37-task HARP VLA fine-tuning run. It predicts absolute Franka joint-position targets plus gripper state (qpos_target_abs, action dimension 8).

Repository: zimplex/harp-vla-train37-act
Final step: 100,000
Final training job: 135212
Final audit job: 135213

The ACT allocation that reached step 80,000 hit its 36-hour wall-time. Training was resumed exactly from the complete 080000 model, optimizer, and RNG state, and the repository was published only after step 100,000 and the replacement final audit passed.

Dataset

Training protocol

All five policies used the same optimizer-step and example budget as the earlier HR-Bench train19 sweep. Every policy consumed exactly 6,400,000 training examples (6,400,000 for this model), approximately 1.46 passes over the 4,373,192-frame dataset. This is not equal-epoch training.

The ACT policy was initialized from scratch apart from the pinned TorchVision ResNet-18 IMAGENET1K_V1 visual backbone.

Pinned software runtime

Optimizer and scheduler

{
  "optimizer": {
    "betas": [
      0.9,
      0.999
    ],
    "eps": 1e-08,
    "grad_clip_norm": 10.0,
    "lr": 1e-05,
    "type": "adamw",
    "weight_decay": 0.0001
  },
  "scheduler": null
}

Policy architecture

Inputs and outputs

The exact, machine-readable training and policy configurations are included as train_config.json and config.json. They are the files emitted by the final checkpoint; their hashes were checked against the immutable completion marker.

Files and loading

The repository root contains the complete pretrained_model/ payload from the LeRobot checkpoint (0.19 GiB), including policy weights, processor state, train_config.json, and config.json. checkpoint_manifest.json records every uploaded checkpoint file's byte size and SHA-256; SHA256SUMS provides the same digests in standard text form. training_provenance.json records the immutable run, dataset, source, job, and audit contract.

Optimizer and RNG files from training_state/ are intentionally not published; this public repository is an inference checkpoint, not a resume bundle.

from lerobot.policies.factory import make_policy_from_pretrained

policy = make_policy_from_pretrained("zimplex/harp-vla-train37-act", device="cuda")
policy.eval()

Use a LeRobot checkout compatible with the configuration included here.

Reproducibility and validation

The final audit required Slurm COMPLETED/0:0, exact marker/config hashes, the pinned dataset and topology, and finite values in every final Safetensors tensor. This model passed with zero audit errors.

Limitations

• This release contains training artifacts, not HARP benchmark evaluation scores. No downstream success rate is claimed by this model card.
• The checkpoint is specific to the converted 20 Hz absolute-qpos convention and its feature/normalization schema.
• No license is asserted by this card; users must comply with the licenses and terms of the base model/backbone, dataset, LeRobot, and other dependencies.