HOI-Retarget — Humanoid Human–Object Interaction Motion

Project page · Code · 3D viewer · Paper · arXiv

Learning from demonstration (LfD) has enabled humanoid robots to acquire diverse whole-body skills, but extending this paradigm to human-object interaction (HOI) is limited by the availability of robot-compatible interaction references. We present HOI-Retarget, a contact-centric retargeting method that transfers HOI onto a humanoid robot for large-scale motion-data generation. Its windowed trajectory optimization uses every labeled contact as a target in the object frame, balancing body tracking, foot support and smoothness under the robot's kinematic limits. The method can augment a single demonstration across object sizes, absorb contacts reconstructed from monocular video, and extend to several robots manipulating one object.

This is the corpus that method produces: 13,904 humanoid trajectories from five human–object interaction capture sets. Each row is one (motion, robot) pair — joint angles, a floating base, the object's 6-DoF pose, and the per-link contact flags that say which palms and feet touch the object at every frame.

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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
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)
Robots	Unitree G1 (29 DoF, object scale 0.83) · Unitree H2 (31 DoF, object scale 1.0)
Passing QC	6,632 G1 (95.4 %) · 6,386 H2 (91.9 %) · 13,018 of 13,904 rows (93.6 %)

Per dataset (G1, object_scale 0.83)

dataset	motions	objects	minutes	qc_pass	keep %
OMOMO	4,421	13	447.7	4,218	95.4
ParaHome	980	12	179.1	969	98.9
NeuralDome	915	20	127.8	852	93.1
CoRoleHOI	456	23	49.1	425	93.2
IMHD²	180	7	24.4	168	93.3
total	6,952	75	828.1	6,632	95.4

Per robot

robot	DoF	object_scale	datasets	rows	qc_pass
unitree_g1	29	0.83	all 5	6,952	6,632 (95.4 %)
unitree_h2	31	1.0	all 5	6,952	6,386 (91.9 %)

The H2 is taller and keeps less: 91.8 % on OMOMO, 97.6 % on ParaHome, 93.2 % on NeuralDome, 77.9 % on CoRoleHOI, 90.0 % on IMHD².

Object meshes

The 13 OMOMO objects ship under assets/objects/ (44 MB of URDF, mesh and surface samples), so the 4,421 OMOMO motions are usable as downloaded. Point the code at this directory:

export HOI_RETARGET_OBJECT_ROOTS=<this download>

The variable adds search roots; do not use HOI_RETARGET_ASSETS, which replaces the whole asset root and hides the robot models.

The other four datasets' meshes reach you through InterAct under CC BY-NC-SA 4.0 with written authorisation and are not redistributed here. Place them under the same root in the layout object_model_path names:

<root>/assets/objects/<object>.urdf       OMOMO — included above
<root>/<dataset>/_assets/<object>.urdf    ParaHome, NeuralDome, CoRoleHOI, IMHD²

The code repository's docs/DATA.md covers obtaining them, and hoi-retarget-stage-object writes the URDF and surface samples for a mesh of your own.

Object names repeat across datasets but the meshes do not, so objects are keyed <dataset>__<object>. docs/objects.jpg is a contact sheet of all 75.

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Quick start

pip install datasets

from datasets import load_dataset
import numpy as np

ds = load_dataset("leggedrobotics/hoi-retarget", split="train")           # everything
ds = load_dataset("leggedrobotics/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"])

Every column is described in docs/SCHEMA.md.

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Quality control

qc_pass is evaluated per (motion, robot): the two robots have different joint limits and fail differently.

qc_pass = False  if  wrist_runfrac > 0.50            # wrist_sustained: a wrist joint pinned beyond
                                                     # 80 % of its own half-range for >50 % of the clip
                 or  foldx_p80 >= 50                 # body_folded: the trunk folds 50 deg further
                                                     # from vertical than the human's did
                 or (limit_sat_pct >= 15 and foldx_p80 >= 15)
                                                     # body_folded: joints against their stops,
                                                     # corroborated by a real fold

foldx_p80 is source-relative — the robot's trunk fold minus the human's — so a person who genuinely squats or sits scores near zero. The flags catch physically implausible motion, not unusual interaction: a clip where the robot kicks a box or never uses its hands passes. Failing rows ship flagged, and qc_flags names the rule that fired (wrist_sustained, body_folded).

One flag is set by hand: object_floating marks the 18 IMHD² skateboard rows (9 motions × 2 robots). In the source data the board sits 20–30 cm above the floor throughout, with the feet on the floor, so no retarget can put the robot on the board.

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Seeing the motions

Every row carries a render of that trajectory in the video column, so the table preview plays each clip in place at 320 × 320 (~50 kB). CoRoleHOI rows show both robots of the pair (this row's robot in the normal colour, the partner tinted).

HOI-Retarget Contact Playback plays any clip in 3D in the browser, with search and filters by dataset, robot and QC result.

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Licensing

CC BY-NC-SA 4.0: non-commercial, attribution, and derivatives carry the same licence. That is the most restrictive term among the sources, and it propagates.

source dataset	its licence
OMOMO	MIT
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

These trajectories are derivative works of the source motion capture, and the contact annotations they were optimised against originate with 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.

The OMOMO object meshes are redistributed from InterMimic under MIT (geometry © 2023 Jiaman Li, assets © 2025 Sirui Xu), notice in assets/objects/LICENSE-OBJECTS. Robot models are Unitree's, BSD-3-Clause (docs/LICENSE-unitree.txt). The retargeting code is BSD-3-Clause.

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Citation

@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},
  journal = {arXiv preprint arXiv:2609.34674},
  year    = {2026},
  url     = {https://arxiv.org/abs/2609.34674}
}

Cite the source dataset your clips come from as well — the dataset column names it, and NOTICE.md lists every reference.