ManiBox: Enhancing Embodied Spatial Generalization via Scalable Simulation Data Generations

Understand

Embodied agents require robust spatial intelligence to execute precise real-world manipulations.

• However, this remains a significant challenge, as current methods often struggle to accurately position objects in space.
• Collecting extensive data can help address this issue by enhancing the agent's spatial understanding.
• Nonetheless, obtaining such data with real robots is prohibitively expensive, and relying on simulation data frequently leads to visual generalization gaps during real-world deployment.