# Baseline Scope

The public benchmark has two formal comparison groups.

## A. Learned World Models

Purpose: compare image-input world-model architectures under the same data, optimizer budget, rollout target, and planning interface.

| Directory | Report Name | Why It Is Included |
|---|---|---|
| `flowmo` | FlowMo | Proposed flow-momentum WM. Separates short object motion state from long ambient drift context. |
| `leworldmodel` | LeWorldModel | Simple JEPA-style latent prediction baseline. Tests whether current-image latent dynamics are enough. |
| `planet` | PlaNet RSSM | Recurrent state-space baseline. Tests whether generic recurrent memory can absorb momentum and drift. |
| `tdmpc2` | TD-MPC2 Dynamics | Compact latent-dynamics baseline. Tests action-conditioned latent rollout with a task-oriented architecture. |

Comparison outputs:

```text
rollout prediction error
heading prediction error
context ablation for FlowMo
planning metrics when the learned WM is used inside the shared planner
```

## B. Traditional Non-WM Controllers

Purpose: compare downstream behavior against hand-designed controllers that do not train a neural world model.

| Directory | Report Name | Why It Is Included |
|---|---|---|
| `pid_los_controller` | PID/LOS controller | Simple classical waypoint tracking baseline. |
| `no_flow_los_controller` | No-Flow LOS Controller | Geometric line-of-sight controller that ignores ambient current. |
| `current_estimator_los_controller` | Current-Estimator LOS Controller | Strong classical baseline that estimates current from recent drift. |
| `oracle_flow_los_controller` | Oracle-Flow LOS Controller | Geometric line-of-sight controller with true local flow feed-forward. |

Comparison outputs:

```text
success rate
final distance
trajectory length over successful episodes
control effort (`sum_t ||a_t||_2^2`) over successful episodes
time to goal over successful episodes
```