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ubr-physical-ai/ubr-maze-nav

ubr-maze-nav — vision + instrument waypoint planning for a small tracked robot Synthetic navigation corpus for fine-tuning small vision-language models to plan local waypoint paths for a 0.3 m tracked ground robot in corridor/maze environments, plus the frozen evaluation suite used in our internal reports. Each sample is one first-person RGB frame (640×480) from the robot's camera in a procedurally generated MuJoCo scene, an instruction carrying the goal (bearing/range) and a… See the full description on the dataset page: https://huggingface.co/datasets/ubr-physical-ai/ubr-maze-nav.

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Dataset Card

ubr-maze-nav — vision + instrument waypoint planning for a small tracked robot

Synthetic navigation corpus for fine-tuning small vision-language models to plan local waypoint paths for a 0.3 m tracked ground robot in corridor/maze environments, plus the frozen evaluation suite used in our internal reports.

Each sample is one first-person RGB frame (640×480) from the robot's camera in a procedurally generated MuJoCo scene, an instruction carrying the goal (bearing/range) and a short instrument channel (prose ranging readings, as the deployed sensor stack reports them), and a completion of ego-frame waypoints (x, y, heading) derived from an omniscient expert planner. Corridor scale, robot pose, lighting, materials, and camera are randomized; 30 % of frames keep a fixed flat rendering. Collision-checked labels only (a validity gate rejects frames whose expert path violates the robot's clearance disc).

Contents

The full corpus lineage is included so every training-data ablation is reproducible. Two pairs share byte-identical images and differ only in the instruction text — they isolate the instrument-channel format: corpus_v1_r/corpus_v1_ri and corpus_v2_fan/corpus_v2_prose.

pathrowsrole
corpus_v0_fixed3 000 + 100fixed 1.4 m cells, aligned pose — the memorizing baseline's data
corpus_v1_r3 000 + 100randomized geometry + appearance, no instrument channel
corpus_v1_ri3 000 + 100+ 3-ray prose ranging (frames identical to v1_r)
corpus_v2_fan3 000 + 10011-ray deg:val fan channel, tight-weighted cells
corpus_v2_prose3 000 + 100prose channel (frames identical to v2_fan)
corpus_v33 000 + 100prose channel, balanced cell mix
corpus_v44 500 + 100prose channel, balanced mix at scale
corpus_v54 500 + 100v4 + 30 % real robot-floor textures (frames shared with v6 base)
corpus_v66 034 + 100prose-fan channel + 1 534 closed-loop DAgger recovery frames — the adapter of record
eval_suite/matrix100frozen evaluation matrix, stratified by decision complexity × corridor scale
eval_suite/matrix_rand100same 100 geometries, training-style appearance
eval_suite/probes/*3 × 60held-out-axis probes (geometry band / texture family / lighting regime)

data.jsonl per split; images referenced relatively. Rows carry the scene seed and exact robot pose, which is how our collision metric reconstructs the true wall grid at eval time.

Intended use & scope

Built for research on instrument-conditioned local planning with compact VLMs (≈4B) on edge hardware. Scenes are kinematic and synthetic; this corpus makes no sim-to-real claim by itself. The generator, training recipes, and result reports are not part of this artifact.

Contact

UB Robotics / teex — via the organization page.

ubr-physical-ai/ubr-maze-nav · CoolFace