Large multimodal models (LMMs) extend large language models (LLMs) with multi-sensory
skills, such as visual understanding, to achieve stronger generic intelligence. In this paper …

For years, researchers have been devoted to generalizable object perception and
manipulation, where cross-category generalizability is highly desired yet underexplored. In …

Articulated object manipulation is a fundamental yet challenging task in robotics. Due to
significant geometric and semantic variations across object categories, previous …

Perceiving and manipulating 3D articulated objects in diverse environments is essential for
home-assistant robots. Recent studies have shown that point-level affordance provides …

Digitizing physical objects into the virtual world has the potential to unlock new research and
applications in embodied AI and mixed reality. This work focuses on recreating interactive …

Abstract We propose Continuous Scene Representations (CSR), a scene representation
constructed by an embodied agent navigating within a space, where objects and their …

The goal of self-supervised visual representation learning is to learn strong, transferable
image representations, with the majority of research focusing on object or scene level. On …

We address the task of simultaneous part-level reconstruction and motion parameter
estimation for articulated objects. Given two sets of multi-view images of an object in two …

We introduce the Universal Manipulation Policy Network (UMPNet)–a single image-based
policy network that infers closed-loop action sequences for manipulating articulated objects …

Understanding and manipulating deformable objects (eg, ropes and fabrics) is an essential
yet challenging task with broad applications. Difficulties come from complex states and …