EgoLAP: Learning from Egocentric Human Data through Language-Action Reasoning

Lihan Zha; Shresth Grover; Tenny Yin; Samuel M. Bateman; Hengkai Pan; Mengchao Zhang; Aykut Onol; Allen Z. Ren; Dhruv Shah; Anirudha Majumdar

arXiv · 2610.08726

EgoLAP: Learning from Egocentric Human Data through Language-Action Reasoning

Lihan Zha·Shresth Grover·Tenny Yin·Samuel M. Bateman·Hengkai Pan·Mengchao Zhang·Aykut Onol·Allen Z. Ren·Dhruv Shah·Anirudha Majumdar

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Abstract

Egocentric human data offer a path to scaling robot learning beyond costly robot demonstrations, yet the embodiment gap makes raw human trajectories a poor supervisory target for control. Our key insight is that, although low-level actions are embodiment-specific, their underlying motion intent can capture task-relevant structure that transfers across humans and robots. We introduce EgoLAP, a VLA pre-training framework that jointly learns from human and robot trajectories through a shared language-based action chain-of-thought. EgoLAP expresses motion intent as structured, temporally abstracted language actions and pairs them with motion-level reasoning grounded in scene geometry, physics, and object affordances. Across extensive real-world and simulated experiments, EgoLAP transfers human experience to robot control more effectively than alternative action representations and reaches 80.1% mean real-world task progress, a 2.3x performance gain over alternative action representations. Motion-level reasoning also outperforms a composite reasoning format that combines subtask, object-box, and visual-trace reasoning.

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Lihan Zha, Shresth Grover, Tenny Yin, Samuel M. Bateman, Hengkai Pan, Mengchao Zhang, Aykut Onol, Allen Z. Ren, Dhruv Shah, Anirudha Majumdar. 2026-10-06. EgoLAP: Learning from Egocentric Human Data through Language-Action Reasoning. https://arxiv.org/abs/2610.08726

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