Visual Navigation Model Checkpoints

Pretrained navigation policies trained with VisNavKit. Each folder holds one model: the Lightning checkpoint, its two ONNX exports (window and streaming), the export metadata (shapes, anchor times, SHA-256 of both files, PyTorch/ONNX parity) and a sample input batch.

Both are FlowPilot-DST policies trained on clips1k (20 Hz, 4 s horizon, point goal with 50% dropout, frozen route VAE, 64 k-means anchors, 4 flow steps). Metrics are on the VisNavKit clips1k validation split, decoded from zero noise. ONNX: fp32, opset 17, batch 1, top-6 plans; PyTorch/ONNX parity is within 1e-5.

Files

<folder>/flowpilot_dst_<encoder>.ckpt           # Lightning checkpoint (weights + optimizer state)
<folder>/flowpilot_dst_<encoder>.onnx           # deployment graph
<folder>/flowpilot_dst_<encoder>.metadata.json  # shapes, anchor times, hashes, parity
<folder>/flowpilot_dst_<encoder>.inputs.npz     # traced sample inputs for a smoke run
<folder>/flowpilot_dst_<encoder>_streaming.{onnx,metadata.json,inputs.npz}  # streaming graph, same weights

Usage

ONNX, no VisNavKit needed:

import numpy as np, onnxruntime as ort
from huggingface_hub import hf_hub_download

repo, stem = "UCLA-VAIL/Visual-Navigation-Model-Checkpoints", "flowpilot-dst-small/flowpilot_dst_fastvit_t12"
session = ort.InferenceSession(hf_hub_download(repo, f"{stem}.onnx"), providers=["CUDAExecutionProvider", "CPUExecutionProvider"])
feeds = dict(np.load(hf_hub_download(repo, f"{stem}.inputs.npz")))  # replace with live data
modes, probs, speed = session.run(["modes", "probs", "speed"], feeds)
x, y, yaw, v, w = modes[0, 0].T  # best plan: 80 steps at 0.05 s, ego frame (x forward, y left)

Inputs are the last 20 frames (1, 20, 3, 216, 384) RGB in [0, 1], route patches, goal, ego speed/yaw rate and action bounds. The full contract, including frame preparation, is in FlowPilot-DST ONNX IO.

Streaming ONNX

*_streaming.onnx runs one frame per call. It caches each past frame's features ([global image feature | route latent]) in a buffer, encodes only the current frame, and decodes only the current plan. After 20 frames its decision equals the window graph's (checked at export: DST exact, DUNE to 5e-7 in ONNX Runtime).

Outputs: modes, probs, speed as in the window graph, plus feat_buffer_out, buffer_mask_out.

stem = "flowpilot-dst-dune/flowpilot_dst_dune_vitb14_streaming"
session = ort.InferenceSession(hf_hub_download(repo, f"{stem}.onnx"))
buf, mask = np.zeros((1, 19, 2048), np.float32), np.zeros((1, 19), np.float32)
for frame, route, goal, ego in stream:  # 20 Hz, frames exactly 50 ms apart
    modes, probs, speed, buf, mask = session.run(None, dict(
        vision=frame[None, None], route_patch=route[None, None], goal=goal[None, None],
        ego=ego[None, None], action_bounds=bounds, feat_buffer=buf, buffer_mask=mask))
    plan = modes[0, 0]  # (80, 5)

• Feed feat_buffer_out and buffer_mask_out back unchanged.
• Call once per frame at 20 Hz. Don't skip frames. Reset the buffer (and, for small, the previous frame) to zeros after a gap or restart.
• Keep one buffer per camera stream.
• CPU latency per call (ONNX Runtime, loaded machine): small 1248 ms window vs 381 ms streaming; dune 7537 ms vs 2021 ms.

Re-export from the checkpoint with VisNavKit (add streaming=true for the streaming graph):

uv run visnavkit-export-dst checkpoint=flowpilot_dst_fastvit_t12.ckpt output=flowpilot_dst_fastvit_t12.onnx

Citation

@Misc{visnavkit2026,
  author       = {Honglin He and Bolei Zhou},
  title        = {{VisNavKit}: a composable toolkit for visual navigation policies},
  howpublished = {\url{https://github.com/VAIL-UCLA/visnavkit}},
  year         = {2026},
}

FlowPilot: arXiv:2606.12603. The DUNE variant uses the DUNE ViT-B/14 encoder.