Contact-rich manipulation requires reliable estimation of extrinsic contacts-the interactions between a grasped object and its environment which provide essential contextual information for planning, control, and policy learning. However, existing approaches often rely on restrictive assumptions, such as predefined contact types, fixed grasp configurations, or camera calibration, that hinder generalization to novel objects and deployment in unstructured environments. In this paper, we present UNIC, a unified multimodal framework for extrinsic contact estimation that operates without any prior knowledge or camera calibration. UNIC directly encodes visual observations in the camera frame and integrates them with proprioceptive and tactile modalities in a fully data-driven manner. It introduces a unified contact representation based on scene affordance maps that captures diverse contact formations and employs a multimodal fusion mechanism with random masking, enabling robust multimodal representation learning. Extensive experiments demonstrate that UNIC performs reliably. It achieves a 9.6 mm average Chamfer distance error on unseen contact locations, performs well on unseen objects, remains robust under missing modalities, and adapts to dynamic camera viewpoints. These results establish extrinsic contact estimation as a practical and versatile capability for contact-rich manipulation. The overview and hardware experiment videos are at https://youtu.be/xpMitkxN6Ls?si=7Vgj-aZ_P1wtnWZN

翻译：接触丰富的操作需要可靠的外部接触估计——即抓取物体与环境之间的相互作用，这些相互作用为规划、控制与策略学习提供了关键的上下文信息。然而，现有方法通常依赖于限制性假设，例如预定义的接触类型、固定的抓取配置或相机标定，这阻碍了其在新物体上的泛化能力以及在非结构化环境中的部署。本文提出UNIC，一个统一的多模态外部接触估计框架，无需任何先验知识或相机标定即可运行。UNIC直接在相机坐标系中对视觉观测进行编码，并以完全数据驱动的方式将其与本体感觉和触觉模态相融合。它引入了一种基于场景可供性图的统一接触表示，该表示能够捕捉多样的接触形态，并采用了一种结合随机掩码的多模态融合机制，从而实现鲁棒的多模态表示学习。大量实验表明，UNIC性能可靠。它在未见过的接触位置上实现了9.6毫米的平均倒角距离误差，在未见过的物体上表现良好，在模态缺失情况下保持鲁棒，并能适应动态的相机视角。这些结果表明，外部接触估计已成为接触丰富操作中一项实用且通用的能力。概览视频与硬件实验视频请访问 https://youtu.be/xpMitkxN6Ls?si=7Vgj-aZ_P1wtnWZN