Model-based reinforcement learning methods often use learning only for the purpose of
estimating an approximate dynamics model, offloading the rest of the decision-making work …

The proliferation of technologies, such as extended reality (XR), has increased the demand
for high-quality three-dimensional (3D) graphical representations. Industrial 3D applications …

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 …

Recent advances in machine learning have led to increased interest in solving visual
computing problems using methods that employ coordinate‐based neural networks. These …

It is a long-standing problem in robotics to develop agents capable of executing diverse
manipulation tasks from visual observations in unstructured real-world environments. To …

We propose CLIP-Fields, an implicit scene model that can be used for a variety of tasks,
such as segmentation, instance identification, semantic search over space, and view …

Performing language-conditioned robotic manipulation tasks in unstructured environments
is highly demanded for general intelligent robots. Conventional robotic manipulation …

We present Neural Descriptor Fields (NDFs), an object representation that encodes both
points and relative poses between an object and a target (such as a robot gripper or a rack …

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 …

To achieve human-level dexterity, robots must infer spatial awareness from multimodal
sensing to reason over contact interactions. During in-hand manipulation of novel objects …