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NOTICE.md

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+# Notice and attribution
+
+This corpus contains **derivative works** — robot trajectories produced by optimising against
+human motion capture and its contact annotations. It is not a redistribution of any source
+dataset's own files, but the sources' terms still govern it.
+
+Released under **CC BY-NC-SA 4.0**: the most restrictive term among the sources, propagated.
+Non-commercial use only; derivatives must carry the same licence.
+
+## Sources — cite the one your clips come from
+
+| dataset | licence | reference |
+|---|---|---|
+| OMOMO | MIT | Li et al., *Object Motion Guided Human Motion Synthesis*, SIGGRAPH Asia 2023 |
+| ParaHome | CC BY-NC-SA 4.0 | Kim et al., *ParaHome*, 2024 |
+| NeuralDome / HODome | CC BY-NC-SA 4.0 | Zhang et al., *NeuralDome*, CVPR 2023 |
+| CoRoleHOI | CC BY 4.0 | Wang et al. |
+| IMHD² | CC BY-NC-SA 4.0 | Zhao et al. |
+
+Contact annotations originate with **InterAct** (Xu et al., CVPR 2025), CC BY-NC-SA 4.0 —
+https://github.com/Sirui-Xu/InterAct. The retargeting objective is built on them.
+
+Robot models: Unitree Robotics, BSD-3-Clause — full text in `docs/LICENSE-unitree.txt`.
+
+The retargeting code is released separately under the BSD 3-Clause Licence,
+Copyright (c) 2026 ETH Zurich: https://github.com/shinben0327/hoi-retarget
+Nothing in it derives from a restrictively licensed source, which is why its terms
+differ from this corpus's.
+
+## Not included
+
+BEHAVE and FORCE (Max Planck terms forbid redistributing modified data without written
+permission), HUMOTO (Adobe Research License v1.1), CHAIRS, and PA-HOI / InterVLA / ReconMot
+(licence could not be verified).

README.md

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+---
+license: cc-by-nc-sa-4.0
+pretty_name: "HOI-Retarget — Humanoid Human-Object Interaction Motion"
+size_categories:
+  - 10K<n<100K
+task_categories:
+  - robotics
+tags:
+  - robotics
+  - humanoid
+  - unitree-g1
+  - unitree-h2
+  - motion-retargeting
+  - human-object-interaction
+  - motion-capture
+  - contact
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: data/*/*/train-*.parquet
+  - config_name: omomo
+    data_files:
+      - split: train
+        path: data/omomo/*/train-*.parquet
+  - config_name: parahome
+    data_files:
+      - split: train
+        path: data/parahome/*/train-*.parquet
+  - config_name: neuraldome
+    data_files:
+      - split: train
+        path: data/neuraldome/*/train-*.parquet
+  - config_name: corolehoi
+    data_files:
+      - split: train
+        path: data/corolehoi/*/train-*.parquet
+  - config_name: imhd2
+    data_files:
+      - split: train
+        path: data/imhd2/*/train-*.parquet
+---
+
+# HOI-Retarget — Humanoid Human–Object Interaction Motion
+
+[Project page](https://shinben0327.github.io/hoi-retarget) · [Code](https://github.com/shinben0327/hoi-retarget) · Paper: *manuscript under review*
+
+> **Reading the data.** Hugging Face's built-in table preview struggles with these
+> rows — they carry deeply nested per-frame arrays, e.g.
+> `fixed_contact_points_per_frame_in_object_frame` is `T × 4 × 3`. Use `datasets`
+> or `pyarrow` directly (see **Quick start**), and to *watch* the motions use the
+> 3D viewer: **[HOI-Retarget Contact Playback](https://huggingface.co/spaces/shinben0327/hoi-retarget-viewer)**.
+
+**Human–object interaction capture, retargeted onto humanoid robots with contact preserved.**
+
+Each row is one **(motion, robot)** pair: a robot trajectory — joint angles, a floating base, and
+the 6-DoF pose of the object being manipulated — together with the **per-link contact flags** that
+say which palms and feet touch the object at each frame.
+
+Retargeting a person carrying a chair onto a humanoid is not a pose-matching problem. The robot is
+shorter, its hands are a rubber block rather than fingers, and a trajectory that merely *looks*
+right drops the object the moment physics is switched on. What makes this data useful is that the
+**contact is right**: every labelled contact is expressed as a target in the object frame and
+recovered by a windowed trajectory optimization that balances it against body tracking, foot
+support and smoothness under the robot's kinematic limits.
+
+---
+
+## Contents
+
+| | |
+|---|---|
+| Source datasets | **5** — OMOMO, ParaHome, NeuralDome, CoRoleHOI, IMHD² |
+| Distinct motions | **6,952** |
+| Robot–motion rows | **13,904** (6,952 G1 + 6,952 H2) |
+| Objects | **75 distinct meshes** (~48 semantic categories) |
+| Duration | **828.1 min · 13.80 h** per robot pass |
+| Collaborative | 912 rows — every CoRoleHOI motion is half of a pair (228 pairs per robot); see `docs/SCHEMA.md` |
+| Robots | Unitree **G1** (29 DoF, object scale 0.83) · Unitree **H2** (31 DoF, object scale 1.0) |
+| Passing QC | **6,463 G1 (93.0 %)** · **6,098 H2 (87.7 %)** |
+
+### Per dataset (G1, `object_scale` 0.83)
+
+| dataset | motions | objects | minutes | qc_pass | keep % |
+|---|---:|---:|---:|---:|---:|
+| OMOMO | 4,421 | 13 | 447.7 | 4,082 | 92.3 |
+| ParaHome | 980 | 12 | 179.1 | 962 | 98.2 |
+| NeuralDome | 915 | 20 | 127.8 | 812 | 88.7 |
+| CoRoleHOI | 456 | 23 | 49.1 | 429 | 94.1 |
+| IMHD² | 180 | 7 | 24.4 | 178 | 98.9 |
+| **total** | **6,952** | **75**\* | **828.1** | **6,463** | **93.0** |
+
+\* Union across datasets. The per-dataset counts sum to 75 and **no mesh is shared between
+datasets** — every one of the 75 was hashed and measured, with zero byte-identical and zero
+geometrically identical pairs. Eight *names* recur (`pan`, `chair`, `suitcase` in three datasets
+each; `book`, `trashcan`, `monitor`, `baseball`, `table` in two), so there are 64 distinct names
+but 75 distinct objects. Merging chair/table/box/case/sofa families gives ~48 semantic categories.
+
+### Per robot
+
+| robot | DoF | object_scale | datasets | rows | qc_pass |
+|---|---:|---:|---|---:|---:|
+| `unitree_g1` | 29 | 0.83 | all 5 | 6,952 | 6,463 (93.0 %) |
+| `unitree_h2` | 31 | 1.0 | all 5 | 6,952 | 6,098 (87.7 %) |
+
+Both robots now cover all five datasets. Per-dataset H2 keep rates: OMOMO 86.9 %, ParaHome 95.1 %,
+NeuralDome 86.4 %, CoRoleHOI 79.6 %, IMHD² 94.4 %.
+
+---
+
+## Quick start
+
+```python
+from datasets import load_dataset
+import numpy as np
+
+ds = load_dataset("shinben0327/hoi-retarget", split="train")          # everything
+ds = load_dataset("shinben0327/hoi-retarget", "omomo", split="train")  # one source dataset
+
+clean = ds.filter(lambda r: r["qc_pass"])          # the curated subset
+g1    = ds.filter(lambda r: r["robot"] == "unitree_g1")
+
+r = clean[0]
+dof = np.array([np.asarray(x) for x in r["dof_pos"]])   # (T, 29) joint angles, radians
+obj = np.array([np.asarray(x) for x in r["object_pos"]])# (T, 3) object position, metres
+print(r["clip_id"], dof.shape, r["object"], r["qc_flags"])
+```
+
+`examples/to_pkl.py` converts a row back to the `contact_window.pkl` layout the
+pipeline consumes — the same file `hoi-retarget --mode contact` writes, so a converted
+row drops straight into [the code repository](https://github.com/shinben0327/hoi-retarget) for re-solving, rendering or
+contact editing.
+
+---
+
+## Quality control
+
+Every row carries a `qc_pass` flag, evaluated per **(motion, robot)** — the two robots have
+different joint limits and fail differently, so this is a property of the pair, not of the motion.
+
+```
+qc_pass = False  if  dj_max_deg    > 60      # peak per-frame joint displacement, deg/frame
+                 or  wrist_runfrac > 0.50    # a wrist joint pinned beyond 80 % of its own
+                                             # half-range for >50 % of the clip
+                 or (wrist_runfrac > 0.20 and palm_contact < 0.20)
+                                             # saturated wrist while the palm is off the object
+```
+
+`palm_contact` is the fraction of that excursion window in which the matching palm is flagged in
+contact. A wrist saturated **with** contact is a hard but legitimate grasp — a deep floor reach or
+an overhead carry. Saturated **without** contact is a retargeting artefact.
+
+**What QC does and does not cover.** These flags mark motion that is *physically implausible* —
+a joint parked against its stop, a discontinuity no body could produce. They do **not** judge the
+interaction itself. A clip where the robot kicks a box, pushes a suitcase or never uses its hands
+is unusual, not defective, and passes QC. Filter on the descriptive columns for that, not `qc_pass`.
+
+Failing rows **are included**, flagged `qc_pass = false`, with the raw metrics
+(`wrist_runfrac`, `dj_max_deg`, `palm_contact`) kept as columns so you can apply your own
+thresholds instead of ours.
+
+## Seeing the motions
+
+Two ways, neither needing a download.
+
+**Inline previews.** Every row carries a 320 x 320 render of that exact trajectory in the `video`
+column — around 50 kB — so the table preview plays each clip in place.
+
+```python
+from datasets import load_dataset, Video
+ds = load_dataset("shinben0327/hoi-retarget", split="train")
+ds[0]["video"]                                                      # decoded frames
+ds.cast_column("video", Video(decode=False))[0]["video"]["bytes"]   # raw mp4
+```
+
+**The 3D viewer.** [HOI-Retarget Contact Playback](https://huggingface.co/spaces/shinben0327/hoi-retarget-viewer)
+plays any clip in the corpus in the browser — scrub it, orbit it, and watch which of the robot's
+four contact links is touching the object at each frame. Search and filter by dataset, robot, QC
+result, or collaborative pairs.
+
+It reads its geometry from this repository's `viewer/` directory, which holds the same
+trajectories in a compact playback format: per-body world poses and per-frame contact flags as
+little-endian float16, one file per (motion, robot).
+
+```
+viewer/index.json              every clip, with its agents and metadata (0.3 MB gzipped)
+viewer/shards/<ds>__<robot>.json   the same records grouped, for lazy loading
+viewer/bin/<ds>__<robot>__<subject>.bin
+```
+
+Each `.bin` is written **block-major** — each array whole, one after another, not interleaved per
+frame:
+
+```
+body_pos   (T, NB, 3)   then  body_quat  (T, NB, 4)  wxyz  then
+object_pos (T, 3)       then  object_rot (T, 4)      wxyz  then
+contact    (T, NC)      0.0 / 1.0
+```
+
+so `object_pos` begins at half-offset `T*NB*3 + T*NB*4`. `NB` is 42 for the G1 and 36 for the H2;
+`NC` is 4. Slice by block, not by a per-frame stride — the file is the same size either way, so a
+length check cannot tell the two readings apart.
+
+The 227 collaborative CoRoleHOI clips carry two agents sharing one object. Both agents store their
+own copy of the object track; they agree to 0.35 cm once each agent's `z_offset_m` is applied, and
+a viewer should draw the object once.
+
+## Licensing
+
+This corpus is **CC BY-NC-SA 4.0**: non-commercial, attribution, and derivatives must carry the
+same licence. That is the most restrictive term among the sources, and it propagates.
+
+| source dataset | its licence | note |
+|---|---|---|
+| OMOMO | MIT | the only permissively licensed source |
+| ParaHome | CC BY-NC-SA 4.0 | |
+| NeuralDome / HODome | CC BY-NC-SA 4.0 | |
+| CoRoleHOI | CC BY 4.0 | |
+| IMHD² | CC BY-NC-SA 4.0 | |
+
+Retargeted trajectories are **derivative works** of the source motion capture, and the contact
+annotations they were optimised against originate with
+[InterAct](https://github.com/Sirui-Xu/InterAct) under CC BY-NC-SA 4.0. Cite the source dataset
+for any clip you use — the `dataset` column names it, and `NOTICE.md` gives the reference.
+
+**Not included**, for licensing reasons: BEHAVE and FORCE (MPI terms forbid redistribution of
+modified data), HUMOTO (Adobe Research License), CHAIRS, and PA-HOI / InterVLA / ReconMot
+(licence unverified).
+
+Robot models are Unitree's, BSD-3-Clause — see `docs/LICENSE-unitree.txt`.
+
+**Why this is more restrictive than the code.** The [retargeting code](https://github.com/shinben0327/hoi-retarget) is
+BSD-3-Clause, because nothing in it derives from a restrictively licensed source. These
+trajectories do: they are optimised against source motion capture and its contact
+annotations, so the sources' terms propagate to them. The two licences are independent
+and both are correct.
+
+---
+
+## Citation
+
+```bibtex
+@article{shin2026hoiretarget,
+  title   = {HOI-Retarget: Contact-Centric Retargeting for Human-Object Interaction},
+  author  = {Shin, Jihwan and L\'opez Escoriza, Adri\`a and He, Junzhe and
+             Heyrman, Matthias and Hutter, Marco},
+  year    = {2026},
+  note    = {Manuscript under review},
+  url     = {https://shinben0327.github.io/hoi-retarget}
+}
+```
+
+Also cite the source dataset your clips come from — the `dataset` column names it,
+and `NOTICE.md` lists every reference.

docs/LICENSE-unitree.txt

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+BSD 3-Clause License
+
+Copyright (c) 2016-2022 HangZhou YuShu TECHNOLOGY CO.,LTD. ("Unitree Robotics")
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

docs/SCHEMA.md

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+# Schema
+
+One row = one **(motion, robot)** pair. `T` is the row's `n_frames`.
+
+## Identity and provenance
+| column | type | meaning |
+|---|---|---|
+| `clip_id` | string | `<dataset>/<robot>/<subject>`, unique |
+| `dataset` | string | `omomo` \| `parahome` \| `neuraldome` \| `corolehoi` \| `imhd2` |
+| `robot` | string | `unitree_g1` (29 DoF) \| `unitree_h2` (31 DoF) |
+| `subject` | string | source clip stem, verbatim from the source dataset |
+| `object` | string | object mesh name; **not unique across datasets** — see README |
+| `object_model_path` | string | URDF the retarget used |
+| `projection_mode`, `schema_version` | string, int32 | pipeline metadata |
+
+## Timing and scale
+| column | type | meaning |
+|---|---|---|
+| `n_frames` | int32 | `T` |
+| `fps` | float | 30.0 throughout |
+| `duration_s` | float | `n_frames / fps` |
+| `object_scale` | float | 0.83 (G1) or 1.0 (H2) — mesh + trajectory scale applied to fit the robot's reach |
+| `object_aug_scale` | float | extra augmentation factor, 1.0 unless augmented |
+| `scale_object_mesh`, `scale_object_trajectory` | bool | which of the two the scale was applied to |
+| `video` | `datasets.Video` | a 320 x 320 mp4 of this exact trajectory, ~50 kB, so the table preview plays in place. `cast_column("video", Video(decode=False))` gives the raw bytes |
+
+## Quality control
+| column | type | meaning |
+|---|---|---|
+| `qc_pass` | bool | see the rule in the README; false when `qc_flags` is non-empty |
+| `qc_flags` | list[string] | `wrist_sustained` (runfrac > 0.50), `wrist_no_contact` (runfrac > 0.20 while the palm is off the object), `jump` (> 60 deg/frame) — empty when passing |
+| `wrist_runfrac` | float | longest run with a wrist joint beyond 80 % of its own half-range, as a fraction of the clip |
+| `dj_max_deg` | float | peak per-frame joint displacement, deg/frame |
+| `palm_contact` | float | fraction of that excursion window in which the matching palm is flagged in contact. `NaN` when the clip has no excursion at all (treated as passing) |
+
+## Sequence and collaboration
+
+Every row answers these, whether or not it is collaborative — a solo clip is a sequence of one,
+not a row with holes, so `groupby("sequence_id")` needs no special case.
+
+| column | type | meaning |
+|---|---|---|
+| `sequence_id` | string | the captured event. Robot-agnostic, so grouping by it returns every row from that capture — for a two-actor sequence that is 4 rows (2 actors x 2 robots) |
+| `n_actors` | int32 | 1 or 2 |
+| `actor_role` | string | `solo`, or `female` / `male` |
+| `partner_clip_id` | string | the same-robot partner's `clip_id`; **empty string** (not null) when there is none |
+| `partner_z_offset_cm` | float32 | signed: **this row's object z minus the partner's**. Subtract it from this row's `object_pos` to land in the partner's frame. `0.0` when solo |
+| `pair_composable` | bool | `abs(partner_z_offset_cm) <= 3.0`; `true` when solo |
+
+**Why the offset exists.** CoRoleHOI sequences were captured with two actors and solved one at a
+time. `object_scale` is identical within every pair, but the two halves disagree about where the
+floor is: the source preprocessing estimates each actor's floor from that actor's own first frames.
+The disagreement is a **constant rigid translation**, not a scale error and not a drift — a fixed
+0.23 cm horizontally in every pair, and vertically 0 to 37.6 cm on the G1, 0 to 49.1 cm on the
+H2. It is stored per row because it scales with the pipeline's trajectory scale (G1 0.83,
+H2 1.08444); the H2 offsets are exactly 1.3066x the G1 ones, pair for pair.
+
+```python
+pair = ds.filter(lambda r: r["sequence_id"] == "corolehoi/100_camera__0_191"
+                           and r["robot"] == "unitree_g1")
+# align the partner into this row's frame before composing one scene:
+#   partner_object_pos[:, 2] += row["partner_z_offset_cm"] / 100.0
+```
+
+912 of 13,904 rows are collaborative (228 CoRoleHOI pairs x 2 robots). The other four datasets are
+single-actor.
+
+## Trajectories
+All are lists of per-frame values, length `T`. Reconstruct with
+`np.array([np.asarray(x) for x in row[col]])`.
+
+| column | shape | meaning |
+|---|---|---|
+| `root_pos` | (T, 3) | floating-base position, metres, world frame |
+| `root_rot` | (T, 4) | base orientation, quaternion **wxyz** |
+| `dof_pos` | (T, ndof) | joint angles, radians; order given by `dof_names` |
+| `object_pos` | (T, 3) | object position, metres |
+| `object_rot` | (T, 4) | object orientation, quaternion **wxyz** |
+| `object_vel` | (T, 3) | object linear velocity, m/s |
+| `object_pos_realsize_ref` | (T, 3) | object position before scaling to the robot's reach |
+| `object_min_height_per_frame` | (T,) | lowest point of the object above the floor, metres |
+
+## Contact
+| column | shape | meaning |
+|---|---|---|
+| `contact_link_names` | list[string] | the links contact is tracked on (4 for the G1: two palm pads, two ankles) |
+| `per_link_contact_flags` | (T, n_links) bool | which of those links touch the object each frame |
+| `fixed_contact_points_per_frame_in_object_frame` | (T, n_links, 3) | the point on the object each link is holding, **in the object frame**. `NaN` where the link is not in contact — this is the no-contact sentinel, not corruption |
+| `robot_foot_ground_contact_flags` | (T, 2) bool | left/right foot on the ground |
+| `object_contact_sequence` | (T,) int8 | object in contact with the human/robot |
+| `object_ground_contact_sequence` | (T,) int8 | object resting on the ground |
+| `object_near_floor_flags` | (T,) bool | object within a floor-proximity threshold |
+
+## Gotchas
+* `object` is **not** a key. Three different chairs share the name `chair`; join on `object_model_path` or `(dataset, object)`.
+* Quaternions are **wxyz**, not xyzw.
+* `dof_names` differs between robots (29 vs 31 entries). Never index `dof_pos` by a hard-coded position — look the joint up by name.
+* The entries named `baseball` are baseball **bats**.

examples/to_pkl.py

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+"""Convert a dataset row back to the `contact_window.pkl` layout the pipeline consumes.
+
+    python to_pkl.py omomo/unitree_g1/sub16_largebox_007 out/
+
+The result is byte-compatible with what `hoi-retarget --mode contact` writes, so it can
+be fed straight back into the code repository — rendered with `hoi-retarget-render`,
+inspected with `hoi-retarget-view`, or re-solved from corrected contacts with
+`hoi-retarget-edit-contacts`.
+
+    https://github.com/shinben0327/hoi-retarget
+"""
+import pickle, sys
+from pathlib import Path
+import numpy as np
+from datasets import load_dataset
+
+ARR2D = ['root_pos','root_rot','dof_pos','object_pos','object_rot','object_vel',
+         'object_pos_realsize_ref','robot_foot_ground_contact_flags','per_link_contact_flags']
+ARR1D = ['object_min_height_per_frame','object_near_floor_flags',
+         'object_contact_sequence','object_ground_contact_sequence']
+
+def to_pkl(row):
+    d = {'fps': float(row['fps'])}
+    for k in ARR2D:
+        d[k] = np.array([np.asarray(x) for x in row[k]])
+    for k in ARR1D:
+        d[k] = np.asarray(row[k])
+    d['fixed_contact_points_per_frame_in_object_frame'] = np.array(
+        [[np.asarray(p) for p in fr] for fr in row['fixed_contact_points_per_frame_in_object_frame']])
+    d['dof_names'] = {n: i for i, n in enumerate(row['dof_names'])}
+    d['contact_link_names'] = list(row['contact_link_names'])
+    for k in ('object_scale','object_aug_scale','scale_object_mesh',
+              'scale_object_trajectory','object_model_path'):
+        d[k] = row[k]
+    d['meta'] = {'schema_version': row['schema_version'], 'robot': row['robot'],
+                 'projection_mode': row['projection_mode'], 'source_file': row['clip_id']}
+    return d
+
+if __name__ == '__main__':
+    clip_id, outdir = sys.argv[1], Path(sys.argv[2])
+    ds = load_dataset('shinben0327/hoi-retarget', split='train')
+    hit = ds.filter(lambda r: r['clip_id'] == clip_id)
+    if not len(hit):
+        raise SystemExit(f'no such clip_id: {clip_id}')
+    row = hit[0]
+    outdir.mkdir(parents=True, exist_ok=True)
+    with open(outdir / 'contact_window.pkl', 'wb') as f:
+        pickle.dump(to_pkl(row), f)
+    print(f"wrote {outdir/'contact_window.pkl'}  ({row['n_frames']} frames, qc_pass={row['qc_pass']})")

viewer/bin/corolehoi__unitree_g1__270_suitcase-male__0_183.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dfe20815742fe28855fe876a1b9461f85e8e062298e77075b0fe974bfe824c62
+size 111020

viewer/bin/corolehoi__unitree_h2__21_bigwaterbottle-female__0_167.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5019a79ce9fd874981668395c19a6c663e47cea0e60e8e3eef1824086461db07
+size 87316

viewer/bin/imhd2__unitree_h2__20230901_wangwzh_suitcase_suitcase_lefthand_carry_1_0_1__0_166.bin

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+   "object": "storage_box",
+   "z_offset_m": 0.004548,
+   "obj_agree_raw_cm": 0.545,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/437_cart__0_245",
+   "agents": [
+    "437_cart-female__0_245",
+    "437_cart-male__0_245"
+   ],
+   "object": "cart",
+   "z_offset_m": 0.017172,
+   "obj_agree_raw_cm": 1.743,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/437_storage_box__0_245",
+   "agents": [
+    "437_storage_box-female__0_245",
+    "437_storage_box-male__0_245"
+   ],
+   "object": "storage_box",
+   "z_offset_m": 0.0,
+   "obj_agree_raw_cm": 0.3,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/441_storage_box__0_284",
+   "agents": [
+    "441_storage_box-female__0_284",
+    "441_storage_box-male__0_284"
+   ],
+   "object": "storage_box",
+   "z_offset_m": 0.009978,
+   "obj_agree_raw_cm": 1.042,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/444_storage_box__0_316",
+   "agents": [
+    "444_storage_box-female__0_316",
+    "444_storage_box-male__0_316"
+   ],
+   "object": "storage_box",
+   "z_offset_m": 0.009081,
+   "obj_agree_raw_cm": 0.956,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/445_storage_box__0_288",
+   "agents": [
+    "445_storage_box-female__0_288",
+    "445_storage_box-male__0_288"
+   ],
+   "object": "storage_box",
+   "z_offset_m": 0.000316,
+   "obj_agree_raw_cm": 0.302,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/448_toolbox__0_190",
+   "agents": [
+    "448_toolbox-female__0_190",
+    "448_toolbox-male__0_190"
+   ],
+   "object": "toolbox",
+   "z_offset_m": -0.005731,
+   "obj_agree_raw_cm": 0.647,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/449_toolbox__0_221",
+   "agents": [
+    "449_toolbox-female__0_221",
+    "449_toolbox-male__0_221"
+   ],
+   "object": "toolbox",
+   "z_offset_m": 0.0079,
+   "obj_agree_raw_cm": 0.845,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/451_toolbox__0_221",
+   "agents": [
+    "451_toolbox-female__0_221",
+    "451_toolbox-male__0_221"
+   ],
+   "object": "toolbox",
+   "z_offset_m": 0.0,
+   "obj_agree_raw_cm": 0.3,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/454_toolbox__0_160",
+   "agents": [
+    "454_toolbox-female__0_160",
+    "454_toolbox-male__0_160"
+   ],
+   "object": "toolbox",
+   "z_offset_m": 0.0,
+   "obj_agree_raw_cm": 0.3,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/455_toolbox__0_150",
+   "agents": [
+    "455_toolbox-female__0_150",
+    "455_toolbox-male__0_150"
+   ],
+   "object": "toolbox",
+   "z_offset_m": 0.008961,
+   "obj_agree_raw_cm": 0.945,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/456_toolbox__0_223",
+   "agents": [
+    "456_toolbox-female__0_223",
+    "456_toolbox-male__0_223"
+   ],
+   "object": "toolbox",
+   "z_offset_m": 0.0,
+   "obj_agree_raw_cm": 0.3,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/457_toolbox__0_211",
+   "agents": [
+    "457_toolbox-female__0_211",
+    "457_toolbox-male__0_211"
+   ],
+   "object": "toolbox",
+   "z_offset_m": 0.0,
+   "obj_agree_raw_cm": 0.3,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/458_toolbox__0_138",
+   "agents": [
+    "458_toolbox-female__0_138",
+    "458_toolbox-male__0_138"
+   ],
+   "object": "toolbox",
+   "z_offset_m": 0.027475,
+   "obj_agree_raw_cm": 2.764,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/459_toolbox__0_151",
+   "agents": [
+    "459_toolbox-female__0_151",
+    "459_toolbox-male__0_151"
+   ],
+   "object": "toolbox",
+   "z_offset_m": 0.010461,
+   "obj_agree_raw_cm": 1.088,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/460_toolbox__0_170",
+   "agents": [
+    "460_toolbox-female__0_170",
+    "460_toolbox-male__0_170"
+   ],
+   "object": "toolbox",
+   "z_offset_m": -0.019954,
+   "obj_agree_raw_cm": 2.018,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/461_toolbox__0_211",
+   "agents": [
+    "461_toolbox-female__0_211",
+    "461_toolbox-male__0_211"
+   ],
+   "object": "toolbox",
+   "z_offset_m": 0.0,
+   "obj_agree_raw_cm": 0.3,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/462_toolbox__0_164",
+   "agents": [
+    "462_toolbox-female__0_164",
+    "462_toolbox-male__0_164"
+   ],
+   "object": "toolbox",
+   "z_offset_m": 0.011107,
+   "obj_agree_raw_cm": 1.151,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/464_toolbox__0_284",
+   "agents": [
+    "464_toolbox-female__0_284",
+    "464_toolbox-male__0_284"
+   ],
+   "object": "toolbox",
+   "z_offset_m": 0.0,
+   "obj_agree_raw_cm": 0.3,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/465_toolbox__0_196",
+   "agents": [
+    "465_toolbox-female__0_196",
+    "465_toolbox-male__0_196"
+   ],
+   "object": "toolbox",
+   "z_offset_m": -0.0,
+   "obj_agree_raw_cm": 0.3,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/466_toolbox__0_142",
+   "agents": [
+    "466_toolbox-female__0_142",
+    "466_toolbox-male__0_142"
+   ],
+   "object": "toolbox",
+   "z_offset_m": 0.001918,
+   "obj_agree_raw_cm": 0.356,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/468_toolbox__0_252",
+   "agents": [
+    "468_toolbox-female__0_252",
+    "468_toolbox-male__0_252"
+   ],
+   "object": "toolbox",
+   "z_offset_m": -0.0,
+   "obj_agree_raw_cm": 0.3,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/469_toolbox__0_243",
+   "agents": [
+    "469_toolbox-female__0_243",
+    "469_toolbox-male__0_243"
+   ],
+   "object": "toolbox",
+   "z_offset_m": 0.0,
+   "obj_agree_raw_cm": 0.3,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/48_storage_box__0_183",
+   "agents": [
+    "48_storage_box-female__0_183",
+    "48_storage_box-male__0_183"
+   ],
+   "object": "storage_box",
+   "z_offset_m": 0.0,
+   "obj_agree_raw_cm": 0.3,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/49_storage_box__0_157",
+   "agents": [
+    "49_storage_box-female__0_157",
+    "49_storage_box-male__0_157"
+   ],
+   "object": "storage_box",
+   "z_offset_m": 0.0,
+   "obj_agree_raw_cm": 0.3,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/50_storage_box__0_164",
+   "agents": [
+    "50_storage_box-female__0_164",
+    "50_storage_box-male__0_164"
+   ],
+   "object": "storage_box",
+   "z_offset_m": 0.0,
+   "obj_agree_raw_cm": 0.3,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/51_storage_box__0_176",
+   "agents": [
+    "51_storage_box-female__0_176",
+    "51_storage_box-male__0_176"
+   ],
+   "object": "storage_box",
+   "z_offset_m": 0.0,
+   "obj_agree_raw_cm": 0.3,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/52_storage_box__0_180",
+   "agents": [
+    "52_storage_box-female__0_180",
+    "52_storage_box-male__0_180"
+   ],
+   "object": "storage_box",
+   "z_offset_m": -0.0,
+   "obj_agree_raw_cm": 0.3,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/53_storage_box__0_164",
+   "agents": [
+    "53_storage_box-female__0_164",
+    "53_storage_box-male__0_164"
+   ],
+   "object": "storage_box",
+   "z_offset_m": -0.0,
+   "obj_agree_raw_cm": 0.3,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/54_storage_box__0_170",
+   "agents": [
+    "54_storage_box-female__0_170",
+    "54_storage_box-male__0_170"
+   ],
+   "object": "storage_box",
+   "z_offset_m": 0.0,
+   "obj_agree_raw_cm": 0.3,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/55_storage_box__0_166",
+   "agents": [
+    "55_storage_box-female__0_166",
+    "55_storage_box-male__0_166"
+   ],
+   "object": "storage_box",
+   "z_offset_m": 0.0,
+   "obj_agree_raw_cm": 0.3,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/56_storage_box__0_264",
+   "agents": [
+    "56_storage_box-female__0_264",
+    "56_storage_box-male__0_264"
+   ],
+   "object": "storage_box",
+   "z_offset_m": -0.029198,
+   "obj_agree_raw_cm": 2.935,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/58_storage_box__0_154",
+   "agents": [
+    "58_storage_box-female__0_154",
+    "58_storage_box-male__0_154"
+   ],
+   "object": "storage_box",
+   "z_offset_m": 0.00839,
+   "obj_agree_raw_cm": 0.891,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/60_storage_box__0_143",
+   "agents": [
+    "60_storage_box-female__0_143",
+    "60_storage_box-male__0_143"
+   ],
+   "object": "storage_box",
+   "z_offset_m": -0.029488,
+   "obj_agree_raw_cm": 2.964,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/61_storage_box__0_139",
+   "agents": [
+    "61_storage_box-female__0_139",
+    "61_storage_box-male__0_139"
+   ],
+   "object": "storage_box",
+   "z_offset_m": -0.004137,
+   "obj_agree_raw_cm": 0.511,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/62_storage_box__0_203",
+   "agents": [
+    "62_storage_box-female__0_203",
+    "62_storage_box-male__0_203"
+   ],
+   "object": "storage_box",
+   "z_offset_m": 0.016583,
+   "obj_agree_raw_cm": 1.685,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/63_storage_box__0_238",
+   "agents": [
+    "63_storage_box-female__0_238",
+    "63_storage_box-male__0_238"
+   ],
+   "object": "storage_box",
+   "z_offset_m": 0.025876,
+   "obj_agree_raw_cm": 2.605,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/71_storage_box__0_122",
+   "agents": [
+    "71_storage_box-female__0_122",
+    "71_storage_box-male__0_122"
+   ],
+   "object": "storage_box",
+   "z_offset_m": 0.016541,
+   "obj_agree_raw_cm": 1.681,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/8_bigwaterbottle__0_199",
+   "agents": [
+    "8_bigwaterbottle-female__0_199",
+    "8_bigwaterbottle-male__0_199"
+   ],
+   "object": "bigwaterbottle",
+   "z_offset_m": 0.0,
+   "obj_agree_raw_cm": 0.3,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/97_camera__0_214",
+   "agents": [
+    "97_camera-female__0_214",
+    "97_camera-male__0_214"
+   ],
+   "object": "camera",
+   "z_offset_m": 0.01503,
+   "obj_agree_raw_cm": 1.533,
+   "obj_agree_offset_cm": 0.3
+  },
+  {
+   "id": "corolehoi/unitree_h2/9_bigwaterbottle__0_220",
+   "agents": [
+    "9_bigwaterbottle-female__0_220",
+    "9_bigwaterbottle-male__0_220"
+   ],
+   "object": "bigwaterbottle",
+   "z_offset_m": 0.0,
+   "obj_agree_raw_cm": 0.3,
+   "obj_agree_offset_cm": 0.3
+  }
+ ],
+ "object_glbs_built": [
+  "corolehoi__bar",
+  "corolehoi__bucket",
+  "corolehoi__cart",
+  "corolehoi__computertower",
+  "corolehoi__hairdryer",
+  "corolehoi__iron_box_lid",
+  "corolehoi__mirror",
+  "corolehoi__printer_s130",
+  "corolehoi__sofa",
+  "corolehoi__table",
+  "corolehoi__thermos",
+  "corolehoi__umbrella",
+  "corolehoi__yoga_ball",
+  "neuraldome__baseball",
+  "neuraldome__bigsofa",
+  "neuraldome__keyboard",
+  "neuraldome__monitor",
+  "neuraldome__pan",
+  "neuraldome__pingpong",
+  "neuraldome__pink",
+  "neuraldome__smallsofa",
+  "neuraldome__table",
+  "neuraldome__talltable",
+  "neuraldome__tennis",
+  "omomo__smallbox",
+  "omomo__smalltable",
+  "parahome__book",
+  "parahome__cuttingboard",
+  "parahome__knife",
+  "parahome__laptop",
+  "parahome__potlid"
+ ],
+ "dropped": [
+  {
+   "what": "pair corolehoi/unitree_g1/144_barbell_full-female__0_176 | corolehoi/unitree_g1/144_barbell_full-male__0_176",
+   "why": "object tracks disagree: z-offset-corrected 1.49 cm (> 1 cm; raw 2.68 cm)"
+  },
+  {
+   "what": "pair corolehoi/unitree_g1/145_barbell_full-female__0_150 | corolehoi/unitree_g1/145_barbell_full-male__0_150",
+   "why": "object tracks disagree: z-offset-corrected 1.27 cm (> 1 cm; raw 4.13 cm)"
+  },
+  {
+   "what": "pair corolehoi/unitree_g1/146_barbell_full-female__0_194 | corolehoi/unitree_g1/146_barbell_full-male__0_194",
+   "why": "object tracks disagree: z-offset-corrected 2.44 cm (> 1 cm; raw 4.21 cm)"
+  },
+  {
+   "what": "pair corolehoi/unitree_g1/195_microwave-female__0_171 | corolehoi/unitree_g1/195_microwave-male__0_171",
+   "why": "object tracks disagree: z-offset-corrected 1.32 cm (> 1 cm; raw 2.06 cm)"
+  },
+  {
+   "what": "pair corolehoi/unitree_g1/197_microwave-female__0_303 | corolehoi/unitree_g1/197_microwave-male__0_303",
+   "why": "object tracks disagree: z-offset-corrected 3.06 cm (> 1 cm; raw 4.15 cm)"
+  },
+  {
+   "what": "pair corolehoi/unitree_g1/198_microwave-female__0_194 | corolehoi/unitree_g1/198_microwave-male__0_194",
+   "why": "object tracks disagree: z-offset-corrected 2.70 cm (> 1 cm; raw 4.66 cm)"
+  },
+  {
+   "what": "pair corolehoi/unitree_g1/199_microwave-female__0_190 | corolehoi/unitree_g1/199_microwave-male__0_190",
+   "why": "object tracks disagree: z-offset-corrected 2.90 cm (> 1 cm; raw 4.18 cm)"
+  },
+  {
+   "what": "pair corolehoi/unitree_g1/207_computertower-female__0_191 | corolehoi/unitree_g1/207_computertower-male__0_191",
+   "why": "object tracks disagree: z-offset-corrected 2.66 cm (> 1 cm; raw 4.11 cm)"
+  },
+  {
+   "what": "pair corolehoi/unitree_g1/209_computertower-female__0_172 | corolehoi/unitree_g1/209_computertower-male__0_172",
+   "why": "object tracks disagree: z-offset-corrected 1.40 cm (> 1 cm; raw 2.31 cm)"
+  },
+  {
+   "what": "pair corolehoi/unitree_g1/210_computertower-female__0_148 | corolehoi/unitree_g1/210_computertower-male__0_148",
+   "why": "object tracks disagree: z-offset-corrected 1.93 cm (> 1 cm; raw 4.59 cm)"
+  },
+  {
+   "what": "pair corolehoi/unitree_g1/21_bigwaterbottle-female__0_167 | corolehoi/unitree_g1/21_bigwaterbottle-male__0_167",
+   "why": "object tracks disagree: z-offset-corrected 2.36 cm (> 1 cm; raw 4.21 cm)"
+  },
+  {
+   "what": "pair corolehoi/unitree_g1/25_bigwaterbottle-female__0_198 | corolehoi/unitree_g1/25_bigwaterbottle-male__0_198",
+   "why": "object tracks disagree: z-offset-corrected 2.73 cm (> 1 cm; raw 5.60 cm)"
+  },
+  {
+   "what": "pair corolehoi/unitree_g1/266_suitcase-female__0_188 | corolehoi/unitree_g1/266_suitcase-male__0_188",
+   "why": "object tracks disagree: z-offset-corrected 3.43 cm (> 1 cm; raw 4.50 cm)"
+  },
+  {
+   "what": "pair corolehoi/unitree_g1/268_suitcase-female__0_162 | corolehoi/unitree_g1/268_suitcase-male__0_162",
+   "why": "object tracks disagree: z-offset-corrected 2.46 cm (> 1 cm; raw 3.69 cm)"
+  },
+  {
+   "what": "pair corolehoi/unitree_g1/26_bigwaterbottle-female__0_286 | corolehoi/unitree_g1/26_bigwaterbottle-male__0_286",
+   "why": "object tracks disagree: z-offset-corrected 3.27 cm (> 1 cm; raw 6.04 cm)"
+  },
+  {
+   "what": "pair corolehoi/unitree_g1/275_bucket-female__0_239 | corolehoi/unitree_g1/275_bucket-male__0_239",
+   "why": "object tracks disagree: z-offset-corrected 2.65 cm (> 1 cm; raw 4.75 cm)"
+  },
+  {
+   "what": "pair corolehoi/unitree_g1/28_bigwaterbottle-female__0_193 | corolehoi/unitree_g1/28_bigwaterbottle-male__0_193",
+   "why": "object tracks disagree: z-offset-corrected 3.79 cm (> 1 cm; raw 5.87 cm)"
+  },
+  {
+   "what": "pair corolehoi/unitree_g1/29_bigwaterbottle-female__0_189 | corolehoi/unitree_g1/29_bigwaterbottle-male__0_189",
+   "why": "object tracks disagree: z-offset-corrected 1.28 cm (> 1 cm; raw 2.09 cm)"
+  },
+  {
+   "what": "pair corolehoi/unitree_g1/33_bigwaterbottle-female__0_133 | corolehoi/unitree_g1/33_bigwaterbottle-male__0_133",
+   "why": "object tracks disagree: z-offset-corrected 1.61 cm (> 1 cm; raw 2.45 cm)"
+  },
+  {
+   "what": "pair corolehoi/unitree_g1/421_toolbox-female__0_292 | corolehoi/unitree_g1/421_toolbox-male__0_292",
+   "why": "object tracks disagree: z-offset-corrected 1.36 cm (> 1 cm; raw 1.68 cm)"
+  },
+  {
+   "what": "pair corolehoi/unitree_g1/435_storage_box-female__0_263 | corolehoi/unitree_g1/435_storage_box-male__0_263",
+   "why": "object tracks disagree: z-offset-corrected 1.33 cm (> 1 cm; raw 2.17 cm)"
+  },
+  {
+   "what": "pair corolehoi/unitree_g1/444_storage_box-female__0_316 | corolehoi/unitree_g1/444_storage_box-male__0_316",
+   "why": "object tracks disagree: z-offset-corrected 1.96 cm (> 1 cm; raw 2.54 cm)"
+  },
+  {
+   "what": "pair corolehoi/unitree_g1/446_toolbox-female__0_174 | corolehoi/unitree_g1/446_toolbox-male__0_174",
+   "why": "object tracks disagree: z-offset-corrected 1.98 cm (> 1 cm; raw 2.77 cm)"
+  },
+  {
+   "what": "pair corolehoi/unitree_g1/458_toolbox-female__0_138 | corolehoi/unitree_g1/458_toolbox-male__0_138",
+   "why": "object tracks disagree: z-offset-corrected 2.10 cm (> 1 cm; raw 3.57 cm)"
+  },
+  {
+   "what": "pair corolehoi/unitree_g1/459_toolbox-female__0_151 | corolehoi/unitree_g1/459_toolbox-male__0_151",
+   "why": "object tracks disagree: z-offset-corrected 2.06 cm (> 1 cm; raw 2.65 cm)"
+  },
+  {
+   "what": "pair corolehoi/unitree_g1/460_toolbox-female__0_170 | corolehoi/unitree_g1/460_toolbox-male__0_170",
+   "why": "object tracks disagree: z-offset-corrected 1.84 cm (> 1 cm; raw 3.02 cm)"
+  },
+  {
+   "what": "pair corolehoi/unitree_h2/446_toolbox-female__0_174 | corolehoi/unitree_h2/446_toolbox-male__0_174",
+   "why": "object tracks disagree: z-offset-corrected 2.21 cm (> 1 cm; raw 2.50 cm)"
+  }
+ ],
+ "notes": [],
+ "objects_unused": [
+  "clothesstand",
+  "floorlamp",
+  "largebox",
+  "largetable",
+  "monitor",
+  "plasticbox",
+  "smallbox",
+  "smalltable",
+  "suitcase",
+  "trashcan",
+  "tripod",
+  "whitechair",
+  "woodchair"
+ ]
+}

viewer/shards/imhd2__unitree_g1.json

@@ -0,0 +1 @@
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