zita-ch/supermimic_vibed

选用指南

AI 根据 README 整理 · 2026/10/22 10:21:34

目标用户

面向熟悉机器人学和强化学习的研究人员或开发者，需要参考SuperMimic论文进行复现。

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# SuperMimic reproduction on Isaac Lab 3 (kit-less, MuJoCo-Warp) **Unofficial** reproduction of *SuperMimic: Scaling Humanoid Whole-Body Control with One Differentiable World Model* (arXiv 2610.28480) — no official code is released, so everything here is implemented from the paper. Target experiment: the controlled LAFAN1 comparison (Fig. 4a): 40 G1-retargeted LAFAN1 clips (2.45 h), one GPU; the paper reaches evaluation normalized return 0.82 after 1.2 h on an H100. Author: **Chong Zhang** (ETH Zurich). License: [MIT](LICENSE).

使用方式

通过命令行训练和评估，使用Docker运行，涉及多个脚本。

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│ ├── g1_29dof.xml generated MJCF │ └── meshes/*.STL 38 meshes └── g1_usd/g1_29dof/ generated USD (+ payloads/) ``` Check: `ls data/lafan1_g1 | wc -l` prints `40`, and the clips total 264,705 frames at 30 fps (2.45 h). `tools/test_env.py` checks the converted robot against the reference motions (FK vs. simulator should agree to ~1e-6): ```bash docker exec supermimic python tools/test_env.py ``` ## Usage ```bash CPUS=0-23 ./run_train.sh lafan1_main # training in tmux session 'lafan1_main' (optional CPU pinning)

docker exec -it supermimic tmux attach -t lafan1_main docker exec supermimic tmux new -d -s eval_main "cd /workspace/supermimic && python supermimic/evaluate.py --run_dir logs/lafan1_main --watch" docker exec supermimic python tools/plot.py logs/lafan1_main --out results/success_curve.png docker exec supermimic python supermimic/evaluate.py --run_dir logs/lafan1_main --ckpt logs/lafan1_main/ckpt_014000.pt --record traj.npz docker exec supermimic python tools/render.py traj.npz out.mp4 --clips dance2_subject1 --seconds 10 ```

部署要求

需要Linux系统、NVIDIA GPU（测试于RTX PRO 6000）、Docker及NVIDIA Container Toolkit，约20GB磁盘空间。

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| `logs/<run>/` | not tracked: `train_log.csv`, `eval.csv`, checkpoints, stdout | | `results/` | success curve and highlight GIF (full videos are rendered locally with `tools/render.py`) | ## Setup Neither Isaac Lab nor any data is included in this repository; the steps below fetch them. **Requirements:** Linux, an NVIDIA GPU (tested: RTX PRO 6000 Blackwell, driver 580 / CUDA 13), Docker with the NVIDIA Container Toolkit (`docker run --gpus` must work), ~20 GB of disk for the images and ~0.2 GB for the data.

已知限制

作者指出代码是快速生成且未审查，使用风险自负；不支持在虚拟内存限制下运行，且资产导入需离线转换。

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> **Disclaimer:** this repository was speed-vibecoded in 1 hour with an AI coding > assistant. It > deliberately does not live under any lab's organization, because the code is too much "slop" > for that: fast and unreviewed. Use it at > your own risk. > **One G1 motion tracker for all 40 LAFAN1 clips, trained in only ~1.5 GPU-hours** > (1.47 h on a single RTX PRO 6000): evaluation return **0.829** with **0/40 failed clips**. > For scale, the paper's PPO-in-simulator baseline does not reach return 0.82 within 18 h on up to > 8 GPUs.

* Torso-CoM and friction are randomized once per environment at start-up (BeyondMimic's start-up events); joint offsets are resampled every episode. Episodes time out after 10 s or at the clip end (bootstrapped, not terminal). * Do not run under a virtual-memory `ulimit -v`: Isaac Lab + Warp reserve ~430 GB of *virtual* address space (CUDA VA reservations; RSS ~3 GB) and Warp's CUDA-graph capture needs its mempool, which cannot initialize under such a limit. Cap memory with the container's cgroup (`--memory`) instead. Rootless docker ignores `--cpuset-cpus`; pin CPUs with `taskset`

(`CPUS=...` in `run_train.sh`). * Asset import: the kit-less URDF/MJCF importers fail inside a running Isaac Lab process (`PhysxJointAPI` schema not registered), so the USD is converted once, offline, by `tools/mjcf_to_usd.py` (registers the importer's schema plugin first) and spawned with `UsdFileCfg(variants={"Physics": "mujoco"})`. ## Attribution and licenses * Method: Q. Liao, Y. Shao, J. Long, K. Zakka, R. Horowitz, P. Abbeel, K. Sreenath, *SuperMimic: Scaling Humanoid Whole-Body Control with One Differentiable World Model*,