MimicLite is an efficient, general humanoid motion-tracking system that can train a deployable policy in 3 hours on 8 RTX 4090 GPUs while retaining competitive tracking quality. Under a matched MuJoCo evaluation, MimicLite improves global root tracking over SONIC while achieving comparable local tracking accuracy. The same policy supports low-latency Pico-driven teleoperation and highly dynamic motion tracking on a physical Unitree G1.
The technical report is available at mimic-lite.pdf.
This repository is the project landing page. Training, evaluation, dataset conversion, and deployment instructions are maintained in their respective repositories:
| Component | Repository | Contents |
|---|---|---|
| MimicLite | EGalahad/mimic-lite |
Training, evaluation, policy export, task configs, and learning code. |
| Training framework | Agent-3154/active-adaptation |
Simulation backends, distributed launchers, environments, and shared infrastructure. |
| Motion data toolkit | EGalahad/any4hdmi |
Motion conversion, validation, visualization, and dataset tooling. |
| Deployment runtime | EGalahad/sim2real |
ONNX inference, MuJoCo sim2sim, Pico teleoperation, and Unitree G1 deployment. |
The released checkpoint set contains three PPO policies trained for 4,000 iterations. The wall-clock column reports the 4,000-update training time on RTX 4090 GPUs. The tracking panels below evaluate the released checkpoints listed in the table.
| Policy | Actor hidden dimensions | Parallel environments | Checkpoint | Wall-clock time |
|---|---|---|---|---|
| MimicLite-Huge | [1024, 1024, 1024] |
32 × 8192 |
xua2csee |
3 h 30 min |
| MimicLite-Base | [256, 256, 256] |
8 × 8192 |
iij0q0b5 |
2 h 57 min |
| MimicLite-Small | [128, 128, 128] |
4 × 8192 |
zb9e19ih |
3 h 00 min |
Training-time sources: Huge 55ie49o5, Base 07k900hl, and Small akq50h1n.
Compared with SONIC, MimicLite retains more progress on dynamic LAFAN motions and improves global root tracking while maintaining comparable local tracking accuracy.
Released training datasets are collected in the any4hdmi Hugging Face collection. The BONES-SEED dataset is the exception: to respect its license and redistribution terms, users obtain it from the original source, while EGalahad/any4hdmi provides only the conversion scripts and processing tools.
The sim2real runtime provides a modular observation interface that separates policy-specific input construction from the shared deployment runtime. Integrating a policy requires only an observation class and a YAML specification; the inference, simulator, and robot interfaces remain unchanged. This common path supports integrated MuJoCo evaluation and real-robot execution for MimicLite, HEFT, TeleopIT, Humanoid-GPT, BFM-Zero, SONIC, and TWIST2. Policy inference is decoupled from robot I/O through interchangeable MuJoCo and physical Unitree G1 backends.
This integration repository is released under GPL-3.0-or-later. Component repositories retain their own histories and license files; verify dataset and component licenses before redistribution.