| --- |
| 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 |
|
|
| [Paper](https://huggingface.co/papers/2602.16705) | [Project](https://hero-humanoid.github.io/) | [Code](https://github.com/RunpeiDong/HERO) |
|
|
| `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 |
|
|
| ```sh |
| 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 |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
