# 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.