forcevla-flexiv-tactar-june26

pi0.5 ARX deltapose checkpoint — puttube task (step 29999)

OpenPI π0.5 fine-tuned on ARX X5 single-arm real-robot data for the "Move the test tube from the transparent rack to the yellow wooden rack" task.

• Base model: pi0.5
• Action representation: deltapose (6-DoF delta xyz+rpy + binary-snap gripper)
• Action horizon: 10
• Train data: 415 episodes, 97 483 frames, 30 fps
• Train steps: 30 000 (cosine LR, peak 5e-5)
• Format: Orbax JAX checkpoint (params/ + assets/xzj_data_0511/norm_stats.json)

Inference-only — train_state/ (optimizer + EMA) was stripped to keep the upload small (~12 GB vs full 42 GB).

Download

huggingface-cli download magic0/forcevla-flexiv-tactar-june26 --local-dir ./checkpoints/29999

Deploy

Server code: https://github.com/SII-ZijunX/openpi-inpaint-vla (branch inpaint-vla).

git clone https://github.com/SII-ZijunX/openpi-inpaint-vla.git
cd openpi-inpaint-vla
uv venv && source .venv/bin/activate
uv pip install -e ".[pytorch]"

# IMPORTANT: --asset_id xzj_data_0511 is required because this checkpoint
# stores norm_stats under assets/xzj_data_0511/, not under the original
# absolute training-data path.
python scripts/inference_server_xzj_arx.py \
    --checkpoint_dir ./checkpoints/29999 \
    --asset_id xzj_data_0511 \
    --zmq_port 6789

Internals

• params/ — Orbax/OCDBT sharded weights, load via openpi.models.model.restore_params(ckpt/"params", dtype=jnp.bfloat16)
• assets/xzj_data_0511/norm_stats.json — z-score / quantile stats from training data, required at inference time