TERRA — post-trained policies (LIBERO, and real-robot arms)

OpenVLA-7B with TERRA latent-action phase 2 (latp2_inv labels), post-trained with a flow-matching head and 4 query tokens. One directory per policy: merged weights (model-*.safetensors), action/latent heads (action_decoder.pt), dataset_statistics.json (action un-normalisation), ctrl_meta.json, processor / tokenizer.

Code: https://github.com/DingTianxingjian/Latent_Free — branch newton, public, no token needed.

git clone -b newton https://github.com/DingTianxingjian/Latent_Free.git latent_free && cd latent_free

dir	suite	demos	standard LIBERO (500 ep.)
libero_10_full	libero_10_no_noops	full	89.0
libero_goal_full	libero_goal_no_noops	full	94.4
piper_pick_can_10k	piper_pick_can (real robot, AgileX Piper)	142	n/a — real robot, see its README
press_button_terra_delta	press_button, 4 colours (real robot, Piper) — DELTA actions: send q + delta, see its README	95	n/a — real robot, see its README

Evaluate the LIBERO ones with acdc_la.downstream.vla.run_libero_eval_cont --pretrained_checkpoint <dir>. The real-robot directory carries its own README; the deployment wrapper, the measured chunk-length and jitter settings, and the offline check are in acdc_la/realrobot/ in the repo.

p2fft_20k — the phase-2 pretrained base

The base the policies above are post-trained from, not a deployable policy: OpenVLA-7B fully fine-tuned for 20,000 steps on OXE to predict our continuous latent action (latp2_inv). vla_full.pt (15,081,838,026 B) is the VLA state dict and action_decoder.pt (105,268,830 B) is required too — finetune_libero_ctrl.py:273 initialises the post-training heads from it, so a run without it starts from a random head.

huggingface-cli download Potestates/TERRA --include "p2fft_20k/*" --local-dir .
INIT=./p2fft_20k  ...  bash acdc_la/scripts/run_vla_post2_portable.sh

The matched control is INIT=none, which post-trains the official openvla/openvla-7b with everything else identical — that is how "what did phase 2 buy" is measured.

HANDOFF_press_button.md at the root of this repo is the brief for training the next real-robot task on a Jetstream2 g3.xl.

The method ledger for all of this is LEDGER.md at the repository root.