Neural Radiance Fields (NeRF) are a rapidly growing area of research with wide-ranging
applications in computer vision, graphics, robotics, and more. In order to streamline the …

We survey applications of pretrained foundation models in robotics. Traditional deep
learning models in robotics are trained on small datasets tailored for specific tasks, which …

Transformers have revolutionized vision and natural language processing with their ability to
scale with large datasets. But in robotic manipulation, data is both limited and expensive …

Emerging neural radiance fields (NeRF) are a promising scene representation for computer
graphics, enabling high-quality 3D reconstruction and novel view synthesis from image …

We address the problem of generating realistic 3D motions of humans interacting with
objects in a scene. Our key idea is to create a neural interaction field attached to a specific …

Humans are able to accurately reason in 3D by gathering multi-view observations of the
surrounding world. Inspired by this insight, we introduce a new large-scale benchmark for …

Representing robotic manipulation tasks as constraints that associate the robot and the
environment is a promising way to encode desired robot behaviors. However, it remains …

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 …

Multi-objective optimization problems are ubiquitous in robotics, eg, the optimization of a
robot manipulation task requires a joint consideration of grasp pose configurations …

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 …