Automatic text-to-3D generation that combines Score Distillation Sampling (SDS) with the
optimization of volume rendering has achieved remarkable progress in synthesizing realistic …

Neural radiance fields (NeRFs) refer to a suit of deep neural networks that are used to learn
and represent objects or scenes. Generally speaking, NeRFs have five main characters …

We propose a novel, object-agnostic method for learning a universal policy for dexterous
object grasping from realistic point cloud observations and proprioceptive information under …

We propose a novel type of map for visual navigation, a renderable neural radiance map
(RNR-Map), which is designed to contain the overall visual information of a 3D environment …

Self-supervised and language-supervised image models contain rich knowledge of the
world that is important for generalization. Many robotic tasks, however, require a detailed …

Transparent object perception is a rapidly developing research problem in artificial
intelligence. The ability to perceive transparent objects enables robots to achieve higher …

Robotic manipulation requires accurate perception of the environment, which poses a
significant challenge due to its inherent complexity and constantly changing nature. In this …

Humans excel at transferring manipulation skills across diverse object shapes, poses, and
appearances due to their understanding of semantic correspondences between different …

Grasping occluded objects in cluttered environments is an essential component in complex
robotic manipulation tasks. In this paper, we introduce an AffordanCE-driven Next-Best-View …

Mobile manipulation constitutes a fundamental task for robotic assistants and garners
significant attention within the robotics community. A critical challenge inherent in mobile …