Diffusion generative modeling has become a promising approach for learning robotic
manipulation tasks from stochastic human demonstrations. In this paper we present …

Building effective imitation learning methods that enable robots to learn from limited data
and still generalize across diverse real-world environments is a long-standing problem in …

If a robot masters folding a kitchen towel, we would expect it to master folding a large beach
towel. However, existing policy learning methods that rely on data augmentation still don't …

Equivariance has gained strong interest as a desirable network property that inherently
ensures robust generalization. However, when dealing with complex systems such as …

Contemporary point cloud segmentation approaches largely rely on richly annotated 3D
training data. However, it is both time-consuming and challenging to obtain consistently …

Research into dynamic 3D scene understanding has primarily focused on short-term change
tracking from dense observations while little attention has been paid to long-term changes …

Deep neural networks (DNNs) in 3D scenes show a strong capability of extracting high-level
semantic features and significantly promote research in the 3D field. 3D shapes and scenes …

We propose a co-part segmentation method that takes a set of point clouds of the same
category as input where neither a ground truth label nor a prior network is required. With …

Humans perceive and interact with the world with the awareness of equivariance, facilitating
us in manipulating different objects in diverse poses. For robotic manipulation, such …

Abstract 3D segmentation is a core problem in computer vision and, similarly to many other
dense prediction tasks, it requires large amounts of annotated data for adequate training …