VERA checkpoints

Hosted weights for VERA — Turning Video Models into Generalist Robot Policies — a two-stage system: a video planner that "dreams" the future, and a Jacobian inverse-dynamics model (IDM) that turns the dream into robot actions. VERA hosts only the trained artifacts; frozen upstream pieces (Wan2.1 text-enc/VAE, VGGT) are pulled from their original homes and filtered out at inference.

Code + usage: see the VERA GitHub repo.

Release groups

1 · Panda-sim (MimicGen)

dir	what
mimicgen-wan-1.3b/	MimicGen-specialist WAN video planner (1.3B, t2v→v2v), DiT-only bf16 (~2.8 GB, de-bloated from 17.5 GB) + flow_decoder.ckpt + algo_config.yaml. Pull Wan-AI/Wan2.1-T2V-1.3B base upstream.
idm-mimicgen-285ouq1q/	Jacobian IDM (run 285ouq1q, VGGT-1B) used for the reported MimicGen stack_d0 evaluations; pairs with mimicgen-wan-1.3b/.

Pair these two + cotracker + the gated/adaptive gripper controller to run stack_d0.

2 · PushT — the two-stage DFoT→Jacobian pipeline

dir	what
pusht-dfot/	DFoT video planner (run dvxixf6d) — a U-Net3D flow predictor (~2.4M params), not WAN. + run_config.yaml.
pusht-idm/	PushT Jacobian IDM (run j1j59qzz, ~34.8M params, 2-DOF) + config.yaml.

3 · OMNI — the cross-embodiment WAN planner

dir	what
omni-wan/	OMNI WAN planner (14B i2v→v2v, combined_4env), DiT-only bf16 (~33 GB). Pull Wan-AI/Wan2.1-I2V-14B-480P base upstream.
idm-mimicgen/	MimicGen IDM (x21o0cwe) — the OMNI-default pairing.
idm-droid/	DROID FR3 IDM (7wohna95, SE3-delta 7-DOF).

4 · DROID — real-robot WAN planner

dir	what
wan-droid-14b/	DROID-specialist WAN video planner (14B i2v→v2v, 29 context frames → 24-frame chunks, 3-view canvas), DiT-only bf16 (~31 GB) + algo_config.yaml. Pull Wan-AI/Wan2.1-I2V-14B-480P base upstream. Used by examples/droid_generation.ipynb in the GitHub repo — language-conditioned video generation from real context frames (no server or robot needed).
droid-demo-clips/	Real DROID context clips (~100 MB) loaded by the generation notebook.

Licenses (per checkpoint)

VERA's own contributions (WAN video planners, Jacobian IDM heads, DFoT planner) are Apache-2.0. Two IDM checkpoints additionally bundle the VGGT-1B backbone weights, which are CC-BY-NC-4.0 — so those combined artifacts are non-commercial.

dir	license	why
wan-droid-14b/, omni-wan/, mimicgen-wan-1.3b/	Apache-2.0	WAN planners; inherit Wan2.1 Apache-2.0
pusht-dfot/, pusht-idm/	Apache-2.0	authors' weights; VGGT not bundled
idm-mimicgen/	Apache-2.0	authors' IDM head only; pulls VGGT-1B upstream at runtime (itself CC-BY-NC-4.0)
idm-droid/, idm-mimicgen-285ouq1q/	CC-BY-NC-4.0	bundle VGGT-1B weights in-checkpoint

Frozen upstream pieces (Wan2.1 text-enc/VAE, VGGT-1B) keep their original licenses.

Training data — pusht-packed/ and pusht-noise-packed/

Two packed PushT training sets, same on-disk format (one NPZ per episode with 504×504 JPEG frames, MegaFlow optical flow qint8_zstd_npz-quantized, and per-episode state, plus index.json / format_manifest.json; per-folder provenance in provenance.json, file hashes in SHA256SUMS.txt):

Folder	Episodes	Size	Content	Actions
pusht-packed/	206	~1.9 GB	human-teleop episodes — the Jacobian IDM training set (config_pusht_vggt_fusion_jacobian)	external: read at train time from the original pusht_cchi_v7_replay.zarr (download in the VERA README's install section)
pusht-noise-packed/	18,685	~61 GB	uniform-random exploration episodes — IDM pretraining / data-efficiency ablations	self-contained: state deltas from the embedded traj_state arrays (no external file)

hf download sizhe-lester-li/VERA --include "pusht-packed/*" --local-dir ./vera-ckpts
hf download sizhe-lester-li/VERA --include "pusht-noise-packed/*" --local-dir ./vera-ckpts   # ~61 GB

Setup + training: TRAINING.md — "Get the training data (PushT)".

License note: the pusht-packed source episodes are the MIT-licensed PushT data from the Diffusion Policy benchmark; the pusht-noise-packed source trajectories are the PushT exploration data from DINO-WM (Zhou et al., 2024). Both were re-rendered with gym-pusht; optical-flow annotations were added with MegaFlow. Regeneration reference: scripts/data/pack_pusht.py in the VERA repo (walkthrough: docs/DATA_GENERATION.md).