Liang Pan

GPT-6-Astra achieves unprecedented embodied intelligence performance, yet practical deployment faces significant hurdles in demonstration cost and physical constraints.

LightNav-0 achieves state-of-the-art navigation performance by unifying spatial reasoning and action generation in a single model, eliminating the need for task-specific components.

Models trained with VBVR-Pro not only excel in a controlled task space but also show significant transferability to external benchmarks, revealing critical insights into visual reasoning mechanisms.

ACE-Data-0 reveals that existing models struggle significantly with complex interactions, exposing critical gaps in embodied AI performance.

A single framework now generates simulation-ready 3D assets for rigid, deformable, and articulated objects, unlocking new possibilities for embodied AI and physics-based simulation.

Finally, a method exists to create 3D human-scene interaction models from casual captures that are stable enough for use in physics simulations and deployment on real-world robots.

Unlock real-world embodied AI: EmbodMocap's affordable dual-iPhone setup captures scene-aware human motion data, enabling robots to learn from human actions in everyday environments.