VINE2_sim_sft_pi05

VINE2 sim SFT policy (pi05_base → π⁰)

Supervised finetune of lerobot/pi05_base on 300 simulated demonstrations of "Pick orange box and place it on white box" (ROBOTIS FFW-SG2, left arm).

This is the initial policy π⁰ for a RECAP / π*0.6 style improvement loop: it is rolled out in simulation while a human takes over to correct mistakes, and the resulting corrections train the advantage-conditioned policy.

Numbers

Rolled out in the calibrated simulator, 12 episodes, initial conditions drawn from the training distribution:

Grasping is essentially solved; the remaining failures are in placement — the box is carried to the target and then toppled or set down off-centre.

Training

chunk_size matters: the default of 50 is one second at the paper's 50 Hz, but this data is 10 Hz, where 50 steps is a five second open loop.

Do not use the trainer's best.pt for a BC run — it selects on advantage conditioning accuracy, which is at chance when conditioning is disabled.

Observations

cam_wrist_right from the real robot is omitted: the right arm is not actuated in this task, so the channel is a near-constant image and cannot be calibrated in simulation.

Loading

from safetensors.torch import load_file
state = load_file("model.safetensors")
policy.load_state_dict(state, strict=False)

Weights are bf16 throughout, matching how the policy runs.