EgoHTR: Egocentric 4D Demonstrations of Human Terrain Traversal

• URL: http://arxiv.org/abs/2607.13472v1
• Date: Wed, 15 Jul 2026 06:02:41 GMT
• Title: EgoHTR: Egocentric 4D Demonstrations of Human Terrain Traversal
• Abstract summary: We open-source a reconstruction pipeline capturing 55 scene-aligned 4D human motion sequences in diverse, complex environments.<n>The resulting dataset comprises over 150k frames, which we evaluate against motion-capture ground truth.<n>Our pipeline enables community-driven dataset extensions and factors the problem to help researchers build context-aware robots.
• Score: 63.906929035961134
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deploying humanoid robots in unstructured terrain remains an open problem. While classic reinforcement learning struggles with the sheer complexity of real-world interactions, more promising methods leveraging human priors remain limited to models lacking contextual awareness. The restricted motion synthesis is a direct consequence of existing dataset pipelines failing to capture human-scene sequences in challenging environments. To bridge this gap between humanoid learning and scene reconstruction, we introduce the Egocentric Human-Terrain Reconstruction (EgoHTR) dataset. We develop and open-source a reconstruction pipeline capturing 55 scene-aligned 4D human motion sequences in diverse, complex environments using a multi-sensor setup of egocentric wearables and a portable 3D scanner. The resulting dataset comprises over 150k frames, which we evaluate against motion-capture ground truth, demonstrating state-of-the-art accuracy and establishing a rigorous benchmark for human motion analysis and synthesis. Further, we leverage this data to train perceptive locomotion policies, demonstrating hardware deployment on a Unitree G1 for reconstructed reference motions. Our pipeline enables community-driven dataset extensions and factors the problem to help researchers build foundational, context-aware robots that reliably traverse uneven terrain.

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