Linfei Li, Ruining Hu, Lin Zhang, Zhong Wang, Fengyi Zhang, Ying Shen, Binqiang Wang, Xin Zhang · 2026 · arXiv
DOI: 10.48550/arXiv.2610.29768 · open-access PDF
视觉语言感知在将自然语言与视觉观察相匹配方面取得了令人印象深刻的进展,但将高层次语义定义为部分层次的物理互动仍然是一个挑战。 为了解决这个差距,我们提出了 UniRAP,一个统一的模型,用于推断部分层次的物理效应,并将语言指示绘制成可操作的地理表示。
Machine translation (Meta M2M100); the English original follows.
Vision-language perception has achieved impressive progress in aligning natural language with visual observations, yet grounding high-level semantics into part-level physical interaction remains challenging. To address this gap, we propose UniRAP, a unified model for inferring part-level physical affordances and mapping language instructions to actionable geometric representations. UniRAP formulates this problem as a conditional multimodal generation task, integrating visual inputs, textual instructions, and optional prompts into a shared spatiotemporal representation space through a unified i
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