LiLa-WAM LIBERO baseline (our data, paper recipe): libero_10

baselines/LiLa-WAM_LIBERO_ours/README.md

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+# LiLa-WAM trained on our LIBERO data (baseline, paper recipe)
+
+These are **not official LiLa-WAM weights.** They are a baseline we trained ourselves.
+
+- **Model and code:** the public LiLa-WAM code (github.com/teee000/LiLa-WAM @ `339860f`; paper arXiv 2608.03701).
+- **Data:** the same LIBERO training data our LoopWAM/FastWAM models use.
+- **Why we trained it:** the authors released no LIBERO checkpoint and no LIBERO training code; only a RoboTwin 2.0 model is public.
+- **Recipe:** the paper's LIBERO recipe, as closely as the paper specifies it.
+
+## Files
+
+| Path | Content |
+|---|---|
+| `<suite>/stage2_epoch2_final.pt` | **Final policy for the suite**: the end of stage 2. Use this one. |
+| `<suite>/stage1_epoch5.pt` | End of stage 1, which initializes stage 2. |
+| `<suite>/config_stage{1,2}.yaml` | The exact configs that were trained, saved by `train.py`. |
+| `<suite>/norm_stats.json` | Action/state min-max for that suite. The top-level key `robotwin2` is required by LiLa-WAM's loader. |
+| `<suite>/logs/` | Full training logs, plus per-update loss CSVs. |
+| `task_cond/<suite>/<task>/task_cond.npy` | The task-condition vector (1024-D) for each of the 40 tasks. |
+| `code/` | Data converter, LIBERO dataset adapter, training wrapper, stats and task-condition scripts, base config, launch script. |
+| `manifest.json` | sha256, size, epochs and final loss of every uploaded checkpoint, filled in per suite as each upload is verified. |
+
+**Checkpoint format.** Each `.pt` is the dict that `train.py` saves: `epoch`, `global_step`, `model_state_dict` (the 212.1M-parameter action model, bf16), `optimizer_state_dict`, `scheduler_state_dict` and `loss`. The frozen DINOv3 ViT-L/16 encoder is **not** included. Get it from `facebook/dinov3-vitl16-pretrain-lvd1689m` (DINOv3 license).
+
+## Training data
+
+The data is our LIBERO set:
+- the FastWAM LIBERO regeneration with MuJoCo 3.3.2 and no-op actions removed, in LeRobot v2.1 format;
+- 1,712 demonstrations and 277,713 frames over 40 tasks;
+- per suite: libero_10 388 demos / 104,280 frames, goal 433 / 52,895, object 457 / 67,309, spatial 434 / 53,229.
+
+It was converted to the authors' LIBERO HDF5 layout (`code/convert_ours_to_lilawam_hdf5.py`) as follows:
+- **Images:** the 512×512 agent-view and wrist frames, resized to 256×256 (INTER_AREA) and stored as PNG. The frames are upright, the same orientation as our LeRobot data.
+- **Action:** 6 end-effector deltas, unchanged, plus the gripper converted to raw LIBERO (−1 open, +1 close; = 1 − 2·ours). A policy therefore outputs raw environment actions; do **not** apply FastWAM's gripper flip.
+- **Proprio:** our 8-D `observation.state` (eef position 3, axis-angle 3, gripper qpos 2).
+- **Cleaning:** none. Every frame of every demonstration is used.
+
+## Recipe
+
+| Item | Paper (Sec. 4.1–4.2) | This training |
+|---|---|---|
+| Policies | one per suite | one per suite |
+| Views | third-person + wrist, 256×256 | the same; side by side as one 512×256 image `[agentview \| wrist]` (fusion not described in the paper; the public model takes one image) |
+| Action chunk | 12 | 12 |
+| Vision encoder | frozen DINOv3 ViT-L/16 | the same (layers −12/−8/−4 + CLS/registers, public config) |
+| Optimizer | AdamW (0.9, 0.99), wd 0.01 | the same |
+| Batch, clipping | 128, 1.0 | 128, 1.0 |
+| Loss | flow matching + λff=0.5 future-feature loss, logit-normal t | the same |
+| LR schedule | stage-wise cosine, peak 2e-4 then 5e-5 | stage 1: cosine 2e-4→5e-5; stage 2 (`--init_from`, fresh optimizer and schedule): cosine 5e-5→5e-6 |
+| Epochs | not stated | stage 1: 5 epochs, stage 2: 2 epochs, per suite |
+| Task condition | task vector from DINOv3 | the same: last-layer CLS of the last frame minus the first frame, averaged over a task's demonstrations, on the same composite |
+
+Each run is the public `train.py`, unchanged except that its dataset factory reads LIBERO (`code/train_libero.py`, `code/libero_data.py`). It ran on one H100 80GB at 0.61 s/update, in interactive job 1094056.
+
+## Inference inputs (must match training)
+
+- **Image:** raw LIBERO observations are upside down, so flip both views with `[::-1, ::-1]`. Resize each to 256×256 (INTER_AREA), then join them with `code/libero_data.composite(agent, wrist)`. Feed RGB with ImageNet normalization.
+- **State:** the 8-D state as above. Use the suite's `norm_stats.json` and the task's `task_cond.npy`. Run 10 Euler flow steps (public config).
+
+## Status
+
+- **LIBERO success rates under our matched protocol are pending:** 4 suites × 10 tasks × 50 trials × seeds 42/43/44.
+- **Paper reference:** the paper reports 98.0 / 98.8 / 97.2 / 94.2 (Spatial / Object / Goal / Long), 97.1 average. Those come from the authors' own data and training length, not from these weights.
+- **License:** the LiLa-WAM ModelScope release states Apache-2.0. DINOv3 is under Meta's DINOv3 license and is not redistributed here.

baselines/LiLa-WAM_LIBERO_ours/code/convert_ours_to_lilawam_hdf5.py

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+#!/usr/bin/env python
+"""Convert OUR LIBERO training data (FastWAM/LoopWAM LeRobot v2.1, MuJoCo 3.3.2 no-noops regeneration, 4 suites)
+into the per-episode HDF5 layout of the LiLa-WAM authors' LIBERO release (modelscope yangfan97/LiLa-WAM_Libero,
+inspected 2026-10-30: <suite>/<task_name>/demo_clean/data/episode_XXXXX.hdf5 with
+  action (T,7) float64 | proprio (T,8) float64 | observation/agentview/rgb (T,) PNG bytes 256x256 |
+  observation/robot0_eye_in_hand/rgb (T,) PNG bytes 256x256).
+
+Choices (each recorded in the file attrs):
+- Images: our 512x512 AV1 frames (already upright, same orientation as the authors' frames) resized to 256x256
+  with cv2.INTER_AREA and stored as lossless PNG, as in the authors' release.
+- action: our 6 EEF deltas unchanged; gripper converted from our 0=close/1=open to LIBERO's raw -1=open/+1=close
+  (raw = 1 - 2g), the same flip FastWAM's eval applies, so a trained policy emits raw environment actions.
+- proprio: OUR 8-D observation.state (EEF pos 3, axis-angle 3, gripper joints 2) - what our matched evaluation
+  feeds every model. (The authors' proprio is pos 3 + quaternion 4 + 1.)
+- All episodes are kept (no stationary-segment cleaning).
+Usage: convert_ours_to_lilawam_hdf5.py <out root> [--workers 8] [--limit N]
+"""
+import argparse, glob, json, os, re, sys
+from multiprocessing import Pool
+from pathlib import Path
+
+SRC = "/groups/yshang/an221229/data/FastWAM/libero_mujoco3.3.2"
+SUITES = ["libero_spatial", "libero_object", "libero_goal", "libero_10"]
+
+
+def task_dir_name(text):
+    return re.sub(r"[^a-z0-9]+", "_", text.lower()).strip("_")
+
+
+def frames(path):
+    import av, cv2
+    out = []
+    with av.open(path) as c:
+        for fr in c.decode(video=0):
+            img = cv2.resize(fr.to_ndarray(format="rgb24"), (256, 256), interpolation=cv2.INTER_AREA)
+            ok, buf = cv2.imencode(".png", cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
+            assert ok
+            out.append(buf)
+    return out
+
+
+def convert(job):
+    import h5py, numpy as np, pyarrow.parquet as pq
+    suite, ep, task, out_root = job
+    d = f"{SRC}/{suite}_no_noops_lerobot"
+    out = Path(out_root) / suite / task_dir_name(task) / "demo_clean" / "data" / f"episode_{ep:05d}.hdf5"
+    if out.exists():
+        return str(out), "exists"
+    t = pq.read_table(f"{d}/data/chunk-000/episode_{ep:06d}.parquet", columns=["action", "observation.state", "frame_index"]).to_pydict()
+    action = np.asarray(t["action"], dtype=np.float64)
+    state = np.asarray(t["observation.state"], dtype=np.float64)
+    g = action[:, -1]
+    if not np.isin(g, [0.0, 1.0]).all():
+        raise ValueError(f"{suite} ep {ep}: gripper action not in {{0,1}}")
+    action[:, -1] = 1.0 - 2.0 * g
+    agent = frames(f"{d}/videos/chunk-000/observation.images.image/episode_{ep:06d}.mp4")
+    wrist = frames(f"{d}/videos/chunk-000/observation.images.wrist_image/episode_{ep:06d}.mp4")
+    n = len(action)
+    if len(agent) != n or len(wrist) != n or list(t["frame_index"]) != list(range(n)):
+        raise ValueError(f"{suite} ep {ep}: {n} rows, {len(agent)}/{len(wrist)} frames")
+    out.parent.mkdir(parents=True, exist_ok=True)
+    tmp = out.with_suffix(".tmp")
+    vl = h5py.vlen_dtype(np.uint8)
+    with h5py.File(tmp, "w") as h:
+        h.create_dataset("action", data=action)
+        h.create_dataset("proprio", data=state)
+        for key, imgs in (("observation/agentview/rgb", agent), ("observation/robot0_eye_in_hand/rgb", wrist)):
+            ds = h.create_dataset(key, (n,), dtype=vl)
+            for i, b in enumerate(imgs):
+                ds[i] = b.reshape(-1)
+        h.attrs.update(source=f"{d} episode {ep}", suite=suite, task=task, image="256x256 PNG, INTER_AREA from 512x512",
+                       gripper="raw LIBERO: -1 open, +1 close (= 1 - 2*ours)",
+                       proprio="ours: eef pos(3), axis-angle(3), gripper qpos(2)")
+    os.replace(tmp, out)
+    return str(out), n
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument("out_root"); p.add_argument("--workers", type=int, default=8); p.add_argument("--limit", type=int, default=0)
+    a = p.parse_args()
+    jobs = []
+    for suite in SUITES:
+        d = f"{SRC}/{suite}_no_noops_lerobot"
+        tasks = {json.loads(l)["task_index"]: json.loads(l)["task"] for l in open(f"{d}/meta/tasks.jsonl")}
+        for line in open(f"{d}/meta/episodes.jsonl"):
+            e = json.loads(line)
+            assert len(e["tasks"]) == 1
+            jobs.append((suite, e["episode_index"], e["tasks"][0], a.out_root))
+    if a.limit:
+        jobs = jobs[: a.limit]
+    print(len(jobs), "episodes", flush=True)
+    done = 0
+    with Pool(a.workers) as pool:
+        for path, n in pool.imap_unordered(convert, jobs, chunksize=2):
+            done += 1
+            if done % 50 == 0 or done == len(jobs):
+                print(done, path, n, flush=True)
+    print("DONE", done, flush=True)
+
+
+if __name__ == "__main__":
+    main()

baselines/LiLa-WAM_LIBERO_ours/code/libero_data.py

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+"""LIBERO adapter for the public LiLa-WAM training code (github.com/teee000/LiLa-WAM @339860f, which ships RoboTwin only).
+
+Reads OUR LIBERO data converted by convert_ours_to_lilawam_hdf5.py (authors' LIBERO HDF5 layout):
+  <root>/<suite>/<task>/demo_clean/data/episode_XXXXX.hdf5 : action (T,7), proprio (T,8), observation/{agentview,robot0_eye_in_hand}/rgb PNG
+Only data access changes; sampling, normalization, augmentation and the model are the public code's.
+- The paper's LIBERO policy sees the third-person and wrist views at 256x256. The public model takes one image, so the
+  two views are placed side by side into one 512x256 (W x H) RGB image: [agentview | wrist]. DINOv3 (patch 16, RoPE)
+  then sees 32x16 patches; the future-feature target is the same composite at t + future_frame_offset.
+- Images are decoded to RGB explicitly (our PNGs are standard-colour); the same composite() is used at eval time.
+- task_name = "<suite>/<task dir>" so the 40 LIBERO tasks are distinct task-condition keys.
+"""
+import sys
+from pathlib import Path
+
+import cv2
+import h5py
+import numpy as np
+import torch
+
+LILA = "/groups/yshang/an221229/code/wam_baselines_20261030/LiLa-WAM"
+if LILA not in sys.path:
+    sys.path.insert(0, LILA)
+from dataloader import dataset as D  # noqa: E402
+
+CAMS = ("agentview", "robot0_eye_in_hand")
+
+
+def decode_rgb(buf):
+    return cv2.cvtColor(cv2.imdecode(np.frombuffer(bytes(buf), np.uint8), cv2.IMREAD_COLOR), cv2.COLOR_BGR2RGB)
+
+
+def composite(agent_rgb, wrist_rgb, size=256):
+    """Two HxWx3 uint8 RGB views -> one size x 2*size x 3 image [agentview | wrist]."""
+    def fit(x):
+        return x if x.shape[:2] == (size, size) else cv2.resize(x, (size, size), interpolation=cv2.INTER_AREA)
+    return np.ascontiguousarray(np.concatenate([fit(agent_rgb), fit(wrist_rgb)], axis=1))
+
+
+class LiberoTaskDataset(D.RobotWinTaskDataset):
+    def _load_episodes(self):
+        for root in self.dataset_dirs:
+            for task_dir in sorted(d for d in root.iterdir() if d.is_dir()):
+                for p in sorted((task_dir / "demo_clean" / "data").glob("*.hdf5")):
+                    self.all_episodes.append({"episode_name": p.stem, "task_name": f"{root.name}/{task_dir.name}",
+                                              "hdf5_path": str(p), "split": "demo_clean"})
+        if not self.all_episodes:
+            raise ValueError(f"No episodes in {self.dataset_dirs}")
+
+    def _build_index_map(self):
+        self.valid_indices, self.episode_metadata, off = [], [], 0
+        for ep in self.all_episodes:
+            with h5py.File(ep["hdf5_path"], "r") as f:
+                n = f["action"].shape[0]
+            if n < 2:
+                continue
+            self.episode_metadata.append({"hdf5_path": ep["hdf5_path"], "task_name": ep["task_name"], "length": n,
+                                          "global_start": off, "global_end": off + n})
+            self.valid_indices.append(np.arange(off, off + n, dtype=np.int64))
+            off += n
+        self.valid_indices = np.concatenate(self.valid_indices)
+        self._ep_end_bounds = np.array([e["global_end"] for e in self.episode_metadata])
+
+    def _frames(self, root, t_idx):
+        idx = np.asarray(t_idx)
+        u = np.unique(idx)
+        a, w = root["observation/agentview/rgb"][u], root["observation/robot0_eye_in_hand/rgb"][u]
+        comp = np.stack([composite(decode_rgb(x), decode_rgb(y)) for x, y in zip(a, w)])
+        return comp[np.searchsorted(u, idx)]
+
+    def _load_hdf5_data(self, hdf5_path, query_indices):
+        out = {}
+        with h5py.File(hdf5_path, "r") as root:
+            for key, src, name in (("action", "action", "action_sequence"), ("state", "proprio", "state")):
+                idx = np.asarray(query_indices[key]); u = np.unique(idx)
+                out[name] = torch.from_numpy(root[src][u][np.searchsorted(u, idx)]).float()
+            cam0 = self.camera_names[0]
+            out["frame"] = {cam0: self._frames(root, query_indices[cam0])}
+            if self.use_future_feat and "future_frame" in query_indices:
+                out["future_frame"] = self._frames(root, query_indices["future_frame"])
+        return out
+
+
+def create_dataset(config, val=False):
+    from omegaconf import OmegaConf
+    ff = config.model.get("future_feat", {})
+    return LiberoTaskDataset(
+        dataset_dir=list(config.dataset.dataset_dir), data_mode="clean",
+        indices_config=OmegaConf.to_container(config.dataset.indices_config, resolve=True), val=val,
+        image_aug=config.dataset.image_aug and not val, camera_names=list(config.dataset.camera_names),
+        image_size=tuple(config.dataset.image_size),
+        task_cond_dir=config.dataset.task_cond_dir if config.model.get("use_task_cond", False) else None,
+        use_future_feat=bool(ff.get("enabled", False)) if ff else False,
+        future_frame_offset=config.dataset.get("future_frame_offset", None))
+
+
+def quat2axisangle(quat):
+    """robosuite transform_utils.quat2axisangle (as copied in FastWAM experiments/libero/libero_utils.py)."""
+    import math
+    quat = np.array(quat, dtype=np.float64)
+    quat[3] = min(max(quat[3], -1.0), 1.0)
+    den = np.sqrt(1.0 - quat[3] * quat[3])
+    if math.isclose(den, 0.0):
+        return np.zeros(3)
+    return (quat[:3] * 2.0 * math.acos(quat[3])) / den
+
+
+def libero_obs_to_inputs(obs):
+    """Raw LIBERO env observation -> (composite RGB uint8 256x512x3, state float32 (8,)), matching training:
+    both views rotated 180 deg like FastWAM's get_libero_image (our training frames are upright), each resized to
+    256x256 with INTER_AREA, joined [agentview | wrist]; state = eef pos, axis-angle(eef quat), gripper qpos
+    (FastWAM _extract_sim_state = our observation.state)."""
+    agent = np.ascontiguousarray(obs["agentview_image"][::-1, ::-1])
+    wrist = np.ascontiguousarray(obs["robot0_eye_in_hand_image"][::-1, ::-1])
+    state = np.concatenate((obs["robot0_eef_pos"], quat2axisangle(obs["robot0_eef_quat"]),
+                            obs["robot0_gripper_qpos"])).astype(np.float32)
+    return composite(agent, wrist), state

baselines/LiLa-WAM_LIBERO_ours/code/libero_ours.yaml

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+common:
+  action_dim: 7
+  state_dim: 8
+  action_chunk_size: 12
+  proprio_len: 1
+  num_inference_steps: 10
+  action_execution_horizon: 12
+dataset:
+  type: libero_ours
+  dataset_dir:
+  - /groups/yshang/an221229/data/LiLaWAM_libero_ours_hdf5/libero_spatial
+  - /groups/yshang/an221229/data/LiLaWAM_libero_ours_hdf5/libero_object
+  - /groups/yshang/an221229/data/LiLaWAM_libero_ours_hdf5/libero_goal
+  - /groups/yshang/an221229/data/LiLaWAM_libero_ours_hdf5/libero_10
+  data_mode: clean
+  camera_names:
+  - agentview
+  image_size:
+  - 512
+  - 256
+  task_cond_dir: /groups/yshang/an221229/data/LiLaWAM_libero_ours_taskcond
+  image_aug: false
+  future_frame_offset: null
+  indices_config:
+    state_indices:
+    - 0
+    camera_indices:
+    - 0
+    action_indices:
+    - 0
+    - 1
+    - 2
+    - 3
+    - 4
+    - 5
+    - 6
+    - 7
+    - 8
+    - 9
+    - 10
+    - 11
+model:
+  num_registers: 2
+  use_task_cond: true
+  vision_encoder:
+    checkpoint_path: /groups/yshang/an221229/checkpoints/wam_baselines/lilawam/dinov3_pretrain/dinov3-vitl16-pretrain-lvd1689m
+    feat_layers:
+    - -12
+    - -8
+    - -4
+    include_cls_register: true
+    fusion_mode: concat
+    concat:
+      proj_type: linear
+      pre_norm: true
+      out_dim: null
+  future_feat:
+    enabled: true
+    attach: cond_tokens
+    target_layer: -4
+    num_decoder_layers: 4
+    num_heads: 4
+  use_ig: false
+  ig_layer: 4
+  action_expert:
+    depth: 12
+    hidden_size: 768
+    num_heads: 8
+    vlm_adapter_num_queries: 64
+    adapter_depth: 4
+training:
+  batch_size: 128
+  time_sampler: logit_normal
+  time_mu: 0.0
+  time_sigma: 1.0
+  learning_rate: 0.0002
+  lr_min: 5.0e-05
+  weight_decay: 0.01
+  betas:
+  - 0.9
+  - 0.99
+  grad_clip_norm: 1.0
+  epochs: 20
+  grad_accum_steps: 1
+  save_interval_epoch: 1
+  ig_lambda: 0.0
+  use_next_chunk_pred: false
+  lambda_next_chunk: 0.5
+  lambda_future_feat: 0.5
+  use_vel_weight: false
+  vel_weight_alpha: 0.2
+  vel_weight_sigma: 0.01
+inference:
+  ig_scale: 1.0
+  ig_stop_step: 10
+  smooth_actions: true
+  smooth_sigma: 1.0
+system:
+  num_workers: 12
+  pin_memory: true

baselines/LiLa-WAM_LIBERO_ours/code/openloop_check.py

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+"""Open-loop sanity check of a trained LiLa-WAM LIBERO checkpoint on demonstration frames (held-in training data).
+
+Builds the model exactly as the public robotwin_infer.RobotWinInference does (config, frozen DINOv3, action model,
+strict state-dict load, bf16) and samples action chunks with its own procedure (Gaussian x_0, 10 Euler steps of the
+flow, denormalize, optional B-spline smoothing), batched. Inputs are the training dataset's own samples
+(libero_data.LiberoTaskDataset: [agentview | wrist] composite, 8-D state, task-condition vector), so this checks that
+the checkpoint + stats + task vectors + inference path reproduce the demonstrations. It is NOT a success-rate metric:
+the frames were seen in training. Baseline row: always predicting the suite's mean action.
+Usage: openloop_check.py --config <stage yaml> --ckpt <.pt> --stats <json> --out <json> [--n 512] [--seed 0]
+"""
+import argparse, json, os, sys, time
+import numpy as np
+import torch
+
+HERE = os.path.dirname(os.path.abspath(__file__))
+sys.path.insert(0, HERE)
+import libero_data  # noqa: E402  (puts the LiLa-WAM repo on sys.path)
+from omegaconf import OmegaConf  # noqa: E402
+import robotwin_infer as R  # noqa: E402
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument("--config", required=True); p.add_argument("--ckpt", required=True)
+    p.add_argument("--stats", required=True); p.add_argument("--out", required=True)
+    p.add_argument("--n", type=int, default=512); p.add_argument("--seed", type=int, default=0)
+    p.add_argument("--batch", type=int, default=64)
+    a = p.parse_args()
+    torch.manual_seed(a.seed); np.random.seed(a.seed)
+    cfg = OmegaConf.load(a.config)
+    t0 = time.time()
+    inf = R.RobotWinInference(config_path=a.config, checkpoint_path=a.ckpt, norm_stats_path=a.stats,
+                              device="cuda", dtype=torch.bfloat16, task_name=None)
+    m, dev, dt_ = inf.model, inf.device, inf.dtype
+    ds = libero_data.create_dataset(cfg, val=True)
+    idx = np.random.RandomState(a.seed).choice(len(ds), size=min(a.n, len(ds)), replace=False)
+    L, D, steps_n = cfg.common.action_chunk_size, cfg.common.action_dim, cfg.common.num_inference_steps
+    stats = json.load(open(a.stats))["robotwin2"]
+    mean_action = np.asarray(stats["action"]["mean"], dtype=np.float32)
+    preds, preds_smooth, gts, masks = [], [], [], []
+    for b0 in range(0, len(idx), a.batch):
+        items = [ds[int(i)] for i in idx[b0:b0 + a.batch]]
+        B = len(items)
+        px = torch.stack([it["pixel_values"] for it in items])
+        state = torch.stack([it["state"] for it in items]).to(dev, dt_)
+        tc = torch.stack([it["task_cond"] for it in items]).to(dev, dt_)
+        with torch.no_grad():
+            qpos = m.normalize_state(state if state.dim() == 3 else state.unsqueeze(1))
+            feats = m.get_vision_features(px)
+            x = torch.randn((B, L, D), device=dev, dtype=dt_)
+            ts = torch.linspace(0, 1, steps_n + 1, device=dev, dtype=dt_)
+            for i in range(steps_n):
+                out = m.action_model(t=ts[i].repeat(B), noisy_actions=x, qpos_history=qpos,
+                                     dino_features_list=feats, task_cond=tc)
+                x = x + out["final_pred"] * (ts[i + 1] - ts[i])
+            act = m.denormalize_action(x).float().cpu().numpy()
+        preds.append(act)
+        preds_smooth.append(np.stack([R.bspline_smooth(c, degree=3, num_ctrl_pts=8) for c in act]))
+        gts.append(torch.stack([it["action_sequence"] for it in items]).numpy())
+        masks.append(torch.stack([it["action_mask"] for it in items]).numpy())
+    P, PS, G, M = map(np.concatenate, (preds, preds_smooth, gts, masks))
+    w = M[..., None].astype(np.float32)
+
+    def metrics(pred):
+        err = np.abs(pred - G) * w
+        mae6 = float(err[..., :6].sum() / (w.sum() * 6))
+        grip_ok = (np.sign(pred[..., 6]) == np.sign(G[..., 6])) & M
+        first = np.abs(pred[:, 0, :6] - G[:, 0, :6]).mean()
+        return {"mae_xyzrot": mae6, "mae_xyzrot_first_action": float(first),
+                "gripper_sign_agreement": float(grip_ok.sum() / M.sum()),
+                "mae_per_dim": [float(err[..., d].sum() / w.sum()) for d in range(D)]}
+
+    res = {"checkpoint": a.ckpt, "config": a.config, "stats": a.stats, "samples": int(len(idx)), "seed": a.seed,
+           "valid_action_steps": int(M.sum()), "model_ms_per_sample": None, "seconds": round(time.time() - t0, 1),
+           "raw": metrics(P), "bspline_smoothed": metrics(PS),
+           "max_abs_raw_minus_smoothed": float(np.abs(P - PS).max()),
+           "mean_abs_raw_minus_smoothed": float(np.abs(P - PS).mean()),
+           "baseline_mean_action": metrics(np.broadcast_to(mean_action, G.shape).copy())}
+    json.dump(res, open(a.out, "w"), indent=1)
+    for k in ("raw", "bspline_smoothed", "baseline_mean_action"):
+        r = res[k]
+        print(f"{k:22s} MAE(6 deltas)={r['mae_xyzrot']:.4f}  first-step MAE={r['mae_xyzrot_first_action']:.4f}  "
+              f"gripper sign agreement={r['gripper_sign_agreement']:.4f}")
+
+
+if __name__ == "__main__":
+    main()

baselines/LiLa-WAM_LIBERO_ours/code/stats_libero.py

@@ -0,0 +1,17 @@
+"""Action/state min-max over every frame of the converted LIBERO data, in the JSON layout LiLa-WAM's
+load_norm_stats reads (top key 'robotwin2' is hard-coded there). Usage: stats_libero.py <data root> <out.json> [suite]"""
+import glob, json, sys, time
+import h5py, numpy as np
+SUITE = sys.argv[3] if len(sys.argv) > 3 else "*"  # optional: one suite
+files = sorted(glob.glob(f"{sys.argv[1]}/{SUITE}/*/demo_clean/data/*.hdf5"))
+A, S = [], []
+for p in files:
+    with h5py.File(p, "r") as f:
+        A.append(f["action"][:]); S.append(f["proprio"][:])
+A, S = np.concatenate(A), np.concatenate(S)
+st = lambda x: {"min": x.min(0).tolist(), "max": x.max(0).tolist(), "mean": x.mean(0).tolist(), "std": x.std(0).tolist()}
+json.dump({"robotwin2": {"meta": {"dataset": sys.argv[1], "files": len(files), "frames": len(A),
+                                   "note": "OUR LIBERO (4 suites); key name required by LiLa-WAM load_norm_stats",
+                                   "timestamp": time.strftime("%F %T")},
+                          "action": st(A), "state": st(S)}}, open(sys.argv[2], "w"), indent=1)
+print(len(files), "files", len(A), "frames"); print("action min", np.round(A.min(0), 3), "max", np.round(A.max(0), 3))

baselines/LiLa-WAM_LIBERO_ours/code/task_cond_libero.py

@@ -0,0 +1,29 @@
+"""Task-condition vectors as in LiLa-WAM utils/precompute_task_cond.py (frozen DINOv3, last-layer CLS of the last
+frame minus the first frame, averaged over a task's episodes), on the same [agentview | wrist] composite the policy
+sees. Writes <out>/<suite>/<task>/task_cond.npy. Usage: task_cond_libero.py <data root> <out dir> <dinov3 dir>"""
+import glob, json, os, sys
+from collections import defaultdict
+import h5py, numpy as np, torch
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+from libero_data import composite, decode_rgb
+from transformers import AutoModel
+root, out, ck = sys.argv[1:4]
+sys.path.insert(0, "/groups/yshang/an221229/code/wam_baselines_20261030/LiLa-WAM")
+from utils.precompute_task_cond import preprocess_image
+m = AutoModel.from_pretrained(ck, torch_dtype=torch.bfloat16, local_files_only=True).cuda().eval()
+diffs = defaultdict(list)
+for p in sorted(glob.glob(f"{root}/*/*/demo_clean/data/*.hdf5")):
+    task = "/".join(p.split("/")[-5:-3])
+    with h5py.File(p, "r") as f:
+        a, w = f["observation/agentview/rgb"], f["observation/robot0_eye_in_hand/rgb"]
+        ims = [composite(decode_rgb(a[i]), decode_rgb(w[i])) for i in (0, len(a) - 1)]
+    px = torch.from_numpy(np.concatenate([preprocess_image(x, (512, 256)) for x in ims])).cuda().to(torch.bfloat16)
+    with torch.no_grad():
+        cls = m(pixel_values=px, return_dict=True).last_hidden_state[:, 0].float().cpu().numpy()
+    diffs[task].append(cls[1] - cls[0])
+for task, d in diffs.items():
+    os.makedirs(f"{out}/{task}", exist_ok=True)
+    np.save(f"{out}/{task}/task_cond.npy", np.mean(d, 0).astype(np.float32))
+json.dump({"tasks": {t: len(d) for t, d in diffs.items()}, "dim": int(len(next(iter(diffs.values()))[0])),
+           "image": "[agentview | wrist] 512x256"}, open(f"{out}/meta.json", "w"), indent=1)
+print(len(diffs), "tasks", sum(len(d) for d in diffs.values()), "episodes")

baselines/LiLa-WAM_LIBERO_ours/code/train_job.sh

@@ -0,0 +1,26 @@
+#!/usr/bin/env bash
+# LiLa-WAM on OUR LIBERO data, following the paper's LIBERO recipe (arXiv 2608.03701 Sec. 4.1-4.2):
+# one policy per suite; third-person + wrist views at 256x256; chunk 12; AdamW(0.9,0.99, wd 0.01), batch 128,
+# clip 1.0, lambda_ff 0.5, logit-normal t; stage-wise cosine LR: stage 1 peak 2e-4, stage 2 peak 5e-5 (public
+# train.py --init_from = new stage, fresh schedule). Not stated in the paper, chosen here: epochs per stage 5 + 2,
+# stage-1 floor 5e-5 (public config), stage-2 floor 5e-6, side-by-side view fusion.
+# Runs inside the user's interactive job 1094056; the srun client lives on evuser2.
+export PYTHONPATH=/groups/yshang/an221229/envs/lilawam_venv/lib/python3.10/site-packages
+export OMP_NUM_THREADS=2 TOKENIZERS_PARALLELISM=false HF_HUB_OFFLINE=1
+P=/groups/yshang/an221229/envs/FastWAM/bin/python
+T=/groups/yshang/an221229/code/lilawam_libero/train_libero.py
+RUN=/groups/yshang/an221229/checkpoints/wam_baselines/lilawam/libero_ours_paper_recipe
+echo "[$(date -Is)] start on $(hostname) job ${SLURM_JOB_ID} step ${SLURM_STEP_ID}"
+for s in libero_10 libero_goal libero_object libero_spatial; do
+  ST=/groups/yshang/an221229/data/LiLaWAM_libero_ours_stats_$s.json
+  mkdir -p $RUN/$s && cd $RUN/$s
+  echo "[$(date -Is)] $s stage1 start"
+  $P $T --config $RUN/configs/${s}_stage1.yaml --norm_stats_path $ST --save_dir $RUN/$s/stage1 > $RUN/logs/${s}_stage1.log 2>&1
+  rc=$?; C1=$(ls -t $RUN/$s/stage1/sft_*/checkpoint_epoch_5.pt 2>/dev/null | head -n 1)
+  echo "[$(date -Is)] $s stage1 rc=$rc ckpt=$C1"
+  [ -f "$C1" ] || continue
+  $P $T --config $RUN/configs/${s}_stage2.yaml --norm_stats_path $ST --save_dir $RUN/$s/stage2 --init_from $C1 > $RUN/logs/${s}_stage2.log 2>&1
+  rc=$?; C2=$(ls -t $RUN/$s/stage2/sft_*/checkpoint_epoch_2.pt 2>/dev/null | head -n 1)
+  echo "[$(date -Is)] $s stage2 rc=$rc ckpt=$C2"
+done
+echo "[$(date -Is)] all done"

baselines/LiLa-WAM_LIBERO_ours/code/train_libero.py

@@ -0,0 +1,9 @@
+"""Run the public LiLa-WAM train.py unchanged except that its dataset factory reads LIBERO (libero_data.py).
+Same CLI as train.py: --config --norm_stats_path --save_dir [--resume]."""
+import os, runpy, sys
+HERE = os.path.dirname(os.path.abspath(__file__))
+sys.path.insert(0, HERE)
+import libero_data  # noqa: E402  (puts the LiLa-WAM repo on sys.path)
+from dataloader import dataset as D  # noqa: E402
+D.create_dataset = libero_data.create_dataset
+runpy.run_path(os.path.join(libero_data.LILA, "train.py"), run_name="__main__")

baselines/LiLa-WAM_LIBERO_ours/libero_10/config_stage1.yaml

@@ -0,0 +1,97 @@
+common:
+  action_dim: 7
+  state_dim: 8
+  action_chunk_size: 12
+  proprio_len: 1
+  num_inference_steps: 10
+  action_execution_horizon: 12
+dataset:
+  type: libero_ours
+  dataset_dir:
+  - /groups/yshang/an221229/data/LiLaWAM_libero_ours_hdf5/libero_10
+  data_mode: clean
+  camera_names:
+  - agentview
+  image_size:
+  - 512
+  - 256
+  task_cond_dir: /groups/yshang/an221229/data/LiLaWAM_libero_ours_taskcond
+  image_aug: false
+  future_frame_offset: null
+  indices_config:
+    state_indices:
+    - 0
+    camera_indices:
+    - 0
+    action_indices:
+    - 0
+    - 1
+    - 2
+    - 3
+    - 4
+    - 5
+    - 6
+    - 7
+    - 8
+    - 9
+    - 10
+    - 11
+model:
+  num_registers: 2
+  use_task_cond: true
+  vision_encoder:
+    checkpoint_path: /groups/yshang/an221229/checkpoints/wam_baselines/lilawam/dinov3_pretrain/dinov3-vitl16-pretrain-lvd1689m
+    feat_layers:
+    - -12
+    - -8
+    - -4
+    include_cls_register: true
+    fusion_mode: concat
+    concat:
+      proj_type: linear
+      pre_norm: true
+      out_dim: null
+  future_feat:
+    enabled: true
+    attach: cond_tokens
+    target_layer: -4
+    num_decoder_layers: 4
+    num_heads: 4
+  use_ig: false
+  ig_layer: 4
+  action_expert:
+    depth: 12
+    hidden_size: 768
+    num_heads: 8
+    vlm_adapter_num_queries: 64
+    adapter_depth: 4
+training:
+  batch_size: 128
+  time_sampler: logit_normal
+  time_mu: 0.0
+  time_sigma: 1.0
+  learning_rate: 0.0002
+  lr_min: 5.0e-05
+  weight_decay: 0.01
+  betas:
+  - 0.9
+  - 0.99
+  grad_clip_norm: 1.0
+  epochs: 5
+  grad_accum_steps: 1
+  save_interval_epoch: 1
+  ig_lambda: 0.0
+  use_next_chunk_pred: false
+  lambda_next_chunk: 0.5
+  lambda_future_feat: 0.5
+  use_vel_weight: false
+  vel_weight_alpha: 0.2
+  vel_weight_sigma: 0.01
+inference:
+  ig_scale: 1.0
+  ig_stop_step: 10
+  smooth_actions: true
+  smooth_sigma: 1.0
+system:
+  num_workers: 12
+  pin_memory: true

baselines/LiLa-WAM_LIBERO_ours/libero_10/config_stage2.yaml

@@ -0,0 +1,97 @@
+common:
+  action_dim: 7
+  state_dim: 8
+  action_chunk_size: 12
+  proprio_len: 1
+  num_inference_steps: 10
+  action_execution_horizon: 12
+dataset:
+  type: libero_ours
+  dataset_dir:
+  - /groups/yshang/an221229/data/LiLaWAM_libero_ours_hdf5/libero_10
+  data_mode: clean
+  camera_names:
+  - agentview
+  image_size:
+  - 512
+  - 256
+  task_cond_dir: /groups/yshang/an221229/data/LiLaWAM_libero_ours_taskcond
+  image_aug: false
+  future_frame_offset: null
+  indices_config:
+    state_indices:
+    - 0
+    camera_indices:
+    - 0
+    action_indices:
+    - 0
+    - 1
+    - 2
+    - 3
+    - 4
+    - 5
+    - 6
+    - 7
+    - 8
+    - 9
+    - 10
+    - 11
+model:
+  num_registers: 2
+  use_task_cond: true
+  vision_encoder:
+    checkpoint_path: /groups/yshang/an221229/checkpoints/wam_baselines/lilawam/dinov3_pretrain/dinov3-vitl16-pretrain-lvd1689m
+    feat_layers:
+    - -12
+    - -8
+    - -4
+    include_cls_register: true
+    fusion_mode: concat
+    concat:
+      proj_type: linear
+      pre_norm: true
+      out_dim: null
+  future_feat:
+    enabled: true
+    attach: cond_tokens
+    target_layer: -4
+    num_decoder_layers: 4
+    num_heads: 4
+  use_ig: false
+  ig_layer: 4
+  action_expert:
+    depth: 12
+    hidden_size: 768
+    num_heads: 8
+    vlm_adapter_num_queries: 64
+    adapter_depth: 4
+training:
+  batch_size: 128
+  time_sampler: logit_normal
+  time_mu: 0.0
+  time_sigma: 1.0
+  learning_rate: 5.0e-05
+  lr_min: 5.0e-06
+  weight_decay: 0.01
+  betas:
+  - 0.9
+  - 0.99
+  grad_clip_norm: 1.0
+  epochs: 2
+  grad_accum_steps: 1
+  save_interval_epoch: 1
+  ig_lambda: 0.0
+  use_next_chunk_pred: false
+  lambda_next_chunk: 0.5
+  lambda_future_feat: 0.5
+  use_vel_weight: false
+  vel_weight_alpha: 0.2
+  vel_weight_sigma: 0.01
+inference:
+  ig_scale: 1.0
+  ig_stop_step: 10
+  smooth_actions: true
+  smooth_sigma: 1.0
+system:
+  num_workers: 12
+  pin_memory: true

baselines/LiLa-WAM_LIBERO_ours/libero_10/logs/train_loss_2026-10-30_09-37-52.csv

@@ -0,0 +1,4071 @@
+Epoch,Step,Global_Step,Loss
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+}