MIMIC

Official implementation of "Learning Sidewalk Autopilot from Multi-Scale Imitation with Corrective Behavior Expansion" (ICRA 2026).

MIMIC is a goal-free, long-context sidewalk navigation policy: it takes a short history of RGB frames and predicts a local trajectory for autonomous sidewalk driving.

Pretrained Model

The pretrained MIMIC policy is released in the UCLA-VAIL/Navigation-Model-Zoo-Public model zoo — exported to ONNX (mimic.onnx) behind a unified inference interface.

Property	Value
Goal mode	goal-free
Context	16 RGB frames
Input resolution	288 × 512 (fixed — frames must already be this size; not resized internally)
Normalization	none — pixel values in [0, 1]
Input rate	5 Hz
Output	15 waypoints (x, y, yaw) at non-uniform timestamps 0.2 s–5.0 s; the wrapper keeps the first 13 (~4 s)
Frame	standard: x = forward, y = left, meters

1. Download

pip install -U "huggingface_hub[cli]"
hf download UCLA-VAIL/Navigation-Model-Zoo-Public --include "MIMIC/*" --local-dir ./nav_model_zoo

2. Dependencies

pip install onnxruntime-gpu numpy torch pyyaml   # use onnxruntime instead for CPU-only

⚠️ urbansim is required to import. MIMIC/inference.py runs from urbansim.custom.pp import PurePursuitController at module load, so the urbansim package must be importable. It is only used to construct a helper that the shipped inference path never calls — if you don't have urbansim, comment out that import to run pure ONNX inference.

3. Run inference

import numpy as np
from MIMIC.inference import MIMICNavigator      # run from ./nav_model_zoo

nav = MIMICNavigator(device="cuda")             # device="cpu" if no GPU

# obs: the robot's last 16 RGB frames at 288×512, (1, 16, 3, 288, 512) float32 in [0, 1]
obs = np.random.rand(1, 16, 3, 288, 512).astype(np.float32)

# MIMIC is goal-free — no goal argument
traj, scores = nav.inference_trajectory(obs)    # (1, 1, 13, 2) meters
vw, best     = nav.inference_vw(obs)            # vw: (1, 2) = [v, ω];  best: (1, 13, 2)
nav.reset()                                     # clear PD smoothing between episodes

inference_trajectory returns local waypoints in meters; inference_vw turns the trajectory into a [linear_v, angular_ω] command via a built-in PD controller (tune limits with max_v / max_w at construction). Feed frames in temporal order [t-15, …, t].

Citation

If you find MIMIC helpful for your research, please cite:

@article{he2026learning,
  title={Learning Sidewalk Autopilot from Multi-Scale Imitation with Corrective Behavior Expansion},
  author={He, Honglin and Ma, Yukai and Squicciarini, Brad and Wu, Wayne and Zhou, Bolei},
  journal={arXiv preprint arXiv:2603.22527},
  year={2026}
}