hist3/README.md

metadata

license: cc-by-nc-4.0
tags:
  - robotics
  - vla
  - pi05
  - openpi
  - manipulation

hist3

Three frames at 0.5 s spacing plus a relative-motion state (recent pose deltas rather than an absolute pose).

training config	pi05_frame_hist3
checkpoint step	7,279 of 7,280
samples seen	232,928
batch size	32
peak LR	0.0005, cosine over 7,280 steps
conditioning	3 frame(s), state relative_history
action space	relative EE, world_rotvec, horizon 50
base weights	pi05_base (PyTorch), LoRA r16 all_linear
frame yaw aug	none
normalizer key	mix/tuned_h50_relative_history3

Contents

• model.safetensors — the weights
• norm_stats.json — the quantile normalizer this checkpoint was trained against. Not interchangeable between variants: the absolute-pose and relative-motion state representations have different scales, and using the wrong one silently rescales output.
• train_config.json — the resolved training config
• mixture.json — dataset sampling weights

Loading

from experiments.infer import VariantPolicy
policy = VariantPolicy.load("hist3")
actions = policy.infer(images, poses, prompt)   # absolute EE targets, [horizon, 10]

Provenance

Trained from pi05_base (Physical Intelligence openpi) on cong-lab manipulation data plus a DROID subset. Inherits the licence terms of both.

Caveats

Trained on a 12-hour single-GPU budget, which is roughly 0.05 of one pass over the 4,245,385-sample mixture. These are ablation checkpoints for a specific comparison, not a converged production policy.