# Robot Action-Conditioned Inference Guide
This guide provides instructions on running inference with Cosmos-Predict2.5/robot/action-cond models.
We recommend first reading the [Inference Guide](inference.md).
## Prerequisites
1. [Setup Guide](./setup.md)
## Example
Action conditioned inference does not yet support multi-GPU.
Run inference with example asset:
```bash
python examples/action_conditioned.py -i assets/action_conditioned/basic/inference_params.json -o outputs/action_conditioned/basic
```
For an explanation of all the available parameters run:
```bash
python examples/action_conditioned.py --help
```
## Configuration
The configuration is split into two parts:
1. **Setup Arguments** (`ActionConditionedSetupArguments`): Model-related configuration that typically stays the same across runs
- `model`: Model variant to use (default: robot/multiview)
- `context_parallel_size`: Context parallelism is not supported for action conditioned model. Set context_parallel_size to 1.
- `output_dir`: Output directory for results
- `config_file`: Model configuration file
2. **Inference Arguments** (`ActionConditionedInferenceArguments`): Per-run parameters that can vary
- `input_root`: Root directory containing videos and annotations
- `input_json_sub_folder`: Subdirectory containing JSON annotations
- `chunk_size`: Action chunk size for processing
- `guidance`: Guidance scale for generation
- `action_load_fn`: Function to load action data
- And many more...
## JSON Configuration File
Create a JSON file with your inference parameters:
```json
{
"name": "my_inference",
"input_root": "/path/to/input/data",
"input_json_sub_folder": "annotations",
"save_root": "/path/to/output",
"chunk_size": 12,
"guidance": 7,
"camera_id": "base",
"start": 0,
"end": 100,
"action_load_fn": "cosmos_predict2.action_conditioned.load_default_action_fn"
}
```
## Custom Action Loading
To use a custom action loading function, implement a function following this signature:
```python
def custom_action_load_fn():
def load_fn(json_data: dict, video_path: str, args: ActionConditionedInferenceArguments) -> dict:
# Your custom action loading logic here
return {
"actions": actions, # numpy array of actions
"initial_frame": img_array, # first frame
"video_array": video_array, # full video
"video_path": video_path,
}
return load_fn
```
Then specify it in your JSON config:
```json
{
"action_load_fn": "my_module.custom_action_load_fn"
}
```
## Outputs