flow-matching-landing-policy

Flow-Matching Landing Policy (Pose-Conditioned)

A DiT-style conditional flow-matching transformer trained via behavior cloning from a PD landing controller, in a simplified MuJoCo quadrotor simulation.

This policy imitates a classical PD controller — it is not claimed to outperform it. See the project README for full pipeline details.

Architecture

• Condition: 4-step pose history (relative position + orientation), no privileged velocity
• Output: 8-step action chunks (vx, vy, vz, yaw_rate)
• Conditioning mechanism: AdaLN (adaptive layer norm), timestep + history embeddings
• Trained with standard conditional flow matching (rectified/OT-style linear paths)

Results

• PD baseline success rate: TODO/50
• Policy success rate: TODO/50
• Evaluated on: randomized spawn (±6m xy, 4-6m altitude), randomized yaw

Usage

from model.flow_matching_v1.inference import load_policy, generate_action_chunk
policy = load_policy("model.pt", device)
chunk = generate_action_chunk(policy, pose_history, device)  # (8, 4)

Limitations

• No vision — pose is privileged simulator state, not detected from images
• No disturbance/wind modeling
• Simplified drone dynamics (no rotor-level thrust/torque physics)