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Introducing Gallant: Voxel Grid-based Humanoid Locomotion and Local-navigation across 3D Constrained Terrains 🤖
Project page: gallantloco.github.io
Arxiv: arxiv.org/abs/2511.14625
Gallant is, to our knowledge, the first system to run a single policy that handles full-space constraints — including ground-level barriers, lateral clutter, and overhead obstacles on a humanoid robot.
Instead of elevation maps or depth cameras, Gallant uses a voxel grid built directly from raw LiDAR as its perception representation, giving it inherent 3D coverage of the scene. With our custom LiDAR simulation toolkit (github.com/agent-3154/sim…), we model realistic scans, including returns from the robot’s own moving links, which is crucial for sim-to-real transfer.
On the control side, we use a target-based training scheme rather than standard velocity tracking. The robot is given a goal and learns to discover its own in-path velocities and trajectories, so no external high-frequency command stream is needed during deployment.
The policy itself is intentionally lightweight: just a 3-layer CNN + 3-layer MLP (~0.3M params), running onboard on the Unitree G1’s Orin NX at 50 Hz with no extra compute.
Training takes about 6 hours on 8× NVIDIA RTX 4090 GPUs. The resulting policy transfers directly to the real robot and achieves >90% success rate on most tested terrain types.
Gallant is our “half-way” step toward robust perceptive locomotion — a problem we believe remains fundamental for humanoid robots. We’re now working toward closing the gap to near-100% reliability and expanding the pipeline further.
Code will be fully released soon.
Discussion, feedback, and collaboration are very welcome! 🙌
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