license: apache-2.0
size_categories:
- 1K<n<10K
task_categories:
- robotics
pretty_name: 'hero_bench_v1: HERO humanoid reaching benchmark'
tags:
- humanoid
- whole-body-control
- end-effector-tracking
- unitree-g1
- mujoco
- benchmark
hero_bench_v1 — HERO humanoid end-effector reaching benchmark
hero_bench_v1 is the public end-effector reaching benchmark of HERO (Learning Humanoid End-Effector Control for Visual
Whole-Body Open-Vocabulary Object Grasping, CoRL 2026): 1,298 synthetic whole-body reaching reference motions for the
Unitree G1 humanoid with the Dex3 hand, scored in MuJoCo with the tooling of the HERO release
(scripts/hero_bench.py, sim2sim/bench/). The paper's 180-clip protocol (three table heights × 60 targets, 3 s hold) is contained
byte for byte as the core tier's paper layer (h050 / h074 / h088).
| tier | clips | what it covers |
|---|---|---|
| core | 420 | paper protocol (180) + reaches to 0.60 m, cross-body targets, 0.62 / 0.80 m tables, top-down and tilted grasps |
| extended | 618 | 0.30 / 0.40 m tables, floor pick-up, 1.00 / 1.10 m shelves, wide lateral targets, palm-down / palm-up / fanned grasps, hovering targets, half-speed reaches, 6 s holds, retraction, table-edge targets |
| stress | 260 | bowing reaches to 0.70 m, a 0.25 m table, 1.15 / 1.20 m shelves, 1.33× speed, and a pool of clips with 1.5–3 cm IK terminal residual |
Each clip is a 50 Hz reference (.npz): 0.3 s settle at the rest pose, a 1.5–6 s reach to a world-fixed palm target, a 3 s hold
(6 s in hold6), and for the retract layer a return to the rest pose. BENCH_MANIFEST.json lists every clip with its target
pose, frame indices (reach_end_frame, hold_end_frame, retract_start_frame), acceptance level and IK residual, plus the frozen
evaluation protocol (protocol block, hashed in protocol_sha256). BENCH_REPORT.md is the per-layer summary.
Files
hero_bench_v1_corpus_33542601.tar.gz # the frozen corpus (about 520 MB; 33542601 = first 8 hex of its sha256, full hash in the .sha256 file next to it)
hero_bench_v1/ # the same corpus unpacked, for browsing / partial download
BENCH_MANIFEST.json BENCH_REPORT.md SHA256SUMS DATA_LICENSE tiers/
core/ extended/ stress/ # <stratum>__<clip_id>.npz + per-tier BENCH_MANIFEST.json
Usage
git clone https://github.com/RunpeiDong/HERO && cd HERO && pip install -e ".[bench]"
python scripts/hero_bench.py fetch --url https://huggingface.co/datasets/RunpeiDong/hero_bench/resolve/main/hero_bench_v1_corpus_33542601.tar.gz \
--sha256 33542601ed456ac606f8177ab75892d2e616585ec562f01976cb6af238c9c5d0 --out data/hero_bench_v1
python scripts/hero_bench.py verify --dir data/hero_bench_v1
python scripts/hero_bench.py run --corpus data/hero_bench_v1 --onnx-dir checkpoints/example --sidecar checkpoints/example/model_hero.json \
--out results/example --tiers core # all = core,extended,stress
python scripts/hero_bench.py report --corpus data/hero_bench_v1 --results results/example --card
A result is comparable to published numbers when verify reports every file byte-identical and the same protocol_sha256.
Scores are world-frame palm errors of the reaching hand, open loop and with HERO's closed-loop replanning, with success rates
S7.5 / S5 (open loop) and C3 (closed loop) and Wilson 95 % intervals; see docs/benchmark.md in the code release for the protocol.
Provenance
The clips are generated by the release's whole-body IK reach generator (data_tools/hero_reach_generator.py) from the plan in
data_tools/reach_specs.py (seeds, quotas, acceptance levels) and re-timed deterministically (data_tools/retime_bench.py);
no human motion-capture data is included. The corpus can be rebuilt with scripts/hero_bench.py build; rebuilds are
protocol-equivalent (same targets, frames and acceptance levels) but not guaranteed byte-identical across MuJoCo / solver builds.
License
Apache License 2.0 (see DATA_LICENSE); the code release is MIT-licensed separately.
Citation
@inproceedings{dong2026hero,
title = {{HERO}: Learning Humanoid End-Effector Control for Visual Whole-Body Open-Vocabulary Object Grasping},
author = {Dong, Runpei and Li, Ziyan and Gupta, Arjun and He, Xialin and Gupta, Saurabh},
booktitle = {10th Annual Conference on Robot Learning},
year = {2026},
url = {https://openreview.net/forum?id=gbchkYm28k}
}