| --- |
| library_name: pytorch |
| language: |
| - en |
| tags: |
| - robotics |
| - robodojo |
| - vision-language-action |
| - meituan-robotics |
| --- |
| |
| # Meituan Robotics 0 |
|
|
| [Technical Report](https://arxiv.org/abs/2611.08577) |
|
|
| A RoboDojo dual-arm vision-language-action policy with episode-first RGB and three seconds of CD-LAM visual memory. It predicts 50 actions and executes 16, using continuous gripper widths. |
|
|
| Inference and evaluation use the **Meituan_Robotics_0** adapter in [XPolicyLab](https://github.com/XPolicyLab/XPolicyLab/tree/main/policy/Meituan_Robotics_0). Use the matching adapter supporting the layout below. |
|
|
| ## Model files |
|
|
| ```text |
| base_vlm/ # Qwen configuration, tokenizer and processor only |
| cdlam/model.pt # Frozen visual-memory encoder |
| checkpoints/model.pt # Complete VLA checkpoint, including VLM weights |
| config.yaml # Inference configuration |
| dataset_statistics.json # State/action normalization |
| ``` |
|
|
| The VLM parameters are already included in `checkpoints/model.pt`; no separate base-VLM weights or training initialization are required. |
|
|
| ## Load |
|
|
| Download this repository, install the policy dependencies following the [XPolicyLab adapter README](https://github.com/XPolicyLab/XPolicyLab/blob/main/policy/Meituan_Robotics_0/README.md), and run from a RoboDojo workspace with `XPolicyLab` importable. Python 3.11 and a CUDA GPU are required. |
|
|
| ```python |
| import os |
| from XPolicyLab.policy.Meituan_Robotics_0.model import Model |
| |
| os.environ["ROBODOJO_ASSET_ROOT"] = "/path/to/downloaded/model-repository" |
| policy = Model({ |
| "bench_name": "RoboDojo", "task_name": "stack_bowls", |
| "env_cfg_type": "arx_x5", "action_type": "joint", |
| "ckpt_name": "mr0", "seed": 0, |
| }) |
| ``` |
|
|
| Use XPolicyLab's `eval.sh` for rollouts so that observations are collected at 25 Hz for causal CD-LAM history: |
|
|
| ```bash |
| # From XPolicyLab/, using your existing policy and simulator environments: |
| export ROBODOJO_ASSET_ROOT=/path/to/downloaded/model-repository |
| EVAL_ENV_TYPE=sim bash policy/Meituan_Robotics_0/eval.sh \ |
| RoboDojo stack_bowls mr0 arx_x5 joint 0 \ |
| 0 1 meituan_robotics_0 robodojo |
| ``` |
|
|
| See [XPolicyLab](https://github.com/XPolicyLab/XPolicyLab) for installation, supported observations, deployment and evaluation instructions. |
|
|
| ## File verification |
|
|
| [SHA256SUMS](SHA256SUMS) is the single SHA-256 checksum manifest for this release. It covers every repository file except the manifest itself, including weights, configuration, tokenizer/processor metadata, and documentation. |
|
|
| After downloading the complete repository, run from its root directory: |
|
|
| ```bash |
| sha256sum -c SHA256SUMS |
| ``` |
|
|
