3 accepted papers
Existing Vision-Language-Action (VLA) models typically take 2D images as visual input, which limits their spatial understanding in complex scenes. How can we incorporate 3D information to enhance VLA capabilities? We conduct a pilot study across different observation spaces and visual representation…
While Vision-Language-Action (VLA) models excel in generalist manipulation, they often lack fine-grained spatial awareness and struggle with viewpoint generalization. This limitation largely stems from the reliance on pretrained RGB encoders, which lack explicit geometric cues and prioritize semanti…
Embodied foundation models are gaining increasing attention for their zero-shot generalization, scalability, and adaptability to new tasks through few-shot post-training. However, existing models rely heavily on real-world data, which is costly and labor-intensive to collect. Synthetic data offers a…