| # Xtrainer2 data adapter |
|
|
| This directory contains optional StarVLA configurations and conversion helpers |
| for Xtrainer2 LeRobot data. It does not distribute robot data, checkpoints, or |
| on-robot control software. The ReTime command in the repository root is the |
| recommended entrypoint for proposal generation and training. |
|
|
| The supplied configurations cover pack-phone with absolute end-effector actions |
| and pick-bottle with relative end-effector actions. For bimanual data, the source |
| action contains 16 values: position, quaternion, and gripper for each arm. |
| Rotation conversion expands the model action to 20 values. `pytorch3d` is |
| required for this conversion; install a build matching your Python, PyTorch, |
| and CUDA environment. The additional Python dependencies are listed in |
| `requirements-xtrainer2.txt`. |
|
|
| Set dataset, model, statistics, training budget, and process count for your own |
| environment. Keep the trainer gradient accumulation equal to the Accelerate |
| configuration. Use a full checkpoint directory with `--start resume` when |
| optimizer and scheduler continuation is required. A model file with the default |
| `--start weights` initializes a new optimizer and scheduler. |
|
|
| Policy serving uses the included `deployment/model_server` implementation. A |
| separate client must supply observations and consume actions using the same |
| schema as the selected data configuration. Client source and deployment |
| instructions are outside this artifact. See [ROBOT.md](ROBOT.md) for the data |
| and interface contract. |
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|
