Sim benchmark · 15 models · policies · 0 submitted
Leaderboard
Full benchmark with method, sortable columns and per-model pages: bench.humanoidrobots.training
Two boards. The sim benchmark runs every published model headless each night under one generic controller, so a failure here is a fact about the model and our controller, not a verdict on the robot. The policy board ranks trained policies once they arrive with an eval we can rerun on the same pinned MJCF.
Nightly stand-hold benchmark
11/15 hold a stand · run 2026-09-24 · MuJoCo 3.14.0 WASM
| Robot | Model commit | Stand-hold 10 s | Base drift | RTF | Load | Date | Method |
|---|---|---|---|---|---|---|---|
| Apptronik ApolloApptronik | c96a32dmujoco_menagerie | passCoM min 99% · tilt 1.5° | -1.0 cm | 55.5× | 2.98 s | 2026-09-24 | stand-hold-v1 |
| Berkeley HumanoidUC Berkeley (Hybrid Robotics) | c96a32dmujoco_menagerie | passCoM min 98% · tilt 4.5° | -1.2 cm | 49.0× | 1.30 s | 2026-09-24 | stand-hold-v1 |
| Booster T1Booster Robotics | c96a32dmujoco_menagerie | passCoM min 99% · tilt 4.9° | 0.0 cm | 29.5× | 0.41 s | 2026-09-24 | stand-hold-v1 |
| Booster T1 12-DoFBooster Robotics | da396a0booster_gym | passCoM min 95% · tilt 0.3° | -2.7 cm | 51.8× | 0.39 s | 2026-09-24 | stand-hold-v1 |
| Fourier N1Fourier | c96a32dmujoco_menagerie | passCoM min 100% · tilt 0.7° | -0.1 cm | 38.2× | 2.69 s | 2026-09-24 | stand-hold-v1 |
| PAL TALOSPAL Robotics | c96a32dmujoco_menagerie | passCoM min 99% · tilt 1.9° | -0.3 cm | 6.0× | 0.72 s | 2026-09-24 | stand-hold-v1 |
| ROBOTIS OP3ROBOTIS | c96a32dmujoco_menagerie | passCoM min 93% · tilt 0.6° | -2.1 cm | 33.6× | 1.87 s | 2026-09-24 | stand-hold-v1 |
| Unitree G1Unitree Robotics | c96a32dmujoco_menagerie | passCoM min 100% · tilt 0° | +0.2 cm | 26.4× | 1.09 s | 2026-09-24 | stand-hold-v1 |
| Unitree G1 12-DoFUnitree Robotics | 276801eunitree_rl_gym | passCoM min 100% · tilt 0.8° | -0.2 cm | 42.2× | 1.14 s | 2026-09-24 | stand-hold-v1 |
| Unitree H1Unitree Robotics | c96a32dmujoco_menagerie | passCoM min 100% · tilt 0.7° | -0.4 cm | 41.9× | 0.56 s | 2026-09-24 | stand-hold-v1 |
| Unitree H1 10-DoFUnitree Robotics | 276801eunitree_rl_gym | passCoM min 99% · tilt 0.8° | -1.4 cm | 56.0× | 0.71 s | 2026-09-24 | stand-hold-v1 |
| Agility CassieAgility Robotics | c96a32dmujoco_menagerie | fellCoM min 17% · tilt 88.3° | -84.8 cm | 8.9× | 0.46 s | 2026-09-24 | stand-hold-v1 |
| DropbearHyperspawnfloor-only contact · meshes simplified | afe0ea0dropbear_mjcf | fellCoM min 13% · tilt 103° | +23.3 cm | 0.6× | 8.14 s | 2026-09-24 | stand-hold-v1 |
| PNDbotics Adam LitePNDbotics | c96a32dmujoco_menagerie | fellCoM min 11% · tilt 90.1° | -83.0 cm | 55.1× | 4.13 s | 2026-09-24 | stand-hold-v1 |
| ToddlerBot 2XCStanford | c96a32dmujoco_menagerie | fellCoM min 32% · tilt 149.8° | -19.8 cm | 15.9× | 2.52 s | 2026-09-24 | stand-hold-v1 |
- Method
- stand-hold-v1
- Controller
- The browser sim's hold controller (packages/sim/src/engine.ts), mirrored headless: joint PD toward the stand keyframe (or qpos0) plus an ankle-strategy balance term from torso tilt, per-model gains from the @hs/sim manifest. No policy, no harness, no pushes.
- Pass
- Runs the full 10 s without a physics reset, the centre of mass never drops below 80% of its start height, and it ends within 10% of the start. Base drift is reported alongside.
- Fell
- The centre of mass drops below 60% of its start height at any point, or the physics diverges (NaN / auto-reset). Heights use the whole-body centre of mass because some models put the base origin near the floor.
- RTF
- Simulated seconds divided by wall seconds for the 10 s run, single thread, no rendering. Depends on the machine; see environment.
- Load
- Compile time: writing the pinned files into the WASM filesystem, mesh simplification when the model's runtime says so, MjModel.from_xml_path, and controller setup. Download time is excluded.
- Machine
- local · 13th Gen Intel(R) Core(TM) i9-13980HX ×32 · win32 10.0.26200 x64 · Node v23.11.0
- Data
- Written nightly by the Coach agent (engine) to apps/humanoidrobots.training/data/benchmarks.json and merged through a pull request.
Policy board
- Walk 0
- Walk, rough terrain 0
- Push recovery 0
- Motion imitation (dance) 0
| # | Policy | Robot | Trainer | Eval return | Sim steps | Ran on hardware |
|---|---|---|---|---|---|---|
No policies yet. Zero is the real number. Train one with mjlab and send the checkpoint, its eval video and the task id. The first entry goes here. Set up a run | ||||||