Robot manipulation relies on accurately predicting contact points and end-effector directions
to ensure successful operation. However learning-based robot manipulation trained on a …

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

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 …

If a robot masters folding a kitchen towel, we would also expect it to master folding a beach
towel. However, existing works for policy learning that rely on data set augmentations are …

Conventional end-to-end visual robotic manipulation learning methods often face
challenges related to data inefficiency and limited generalizability. To mitigate these …

Integrating a notion of symmetry into point cloud neural networks is a provably effective way
to improve their generalization capability. Of particular interest are $ E (3) $ equivariant point …

3D modeling of articulated objects is a research problem within computer vision, graphics,
and robotics. Its objective is to understand the shape and motion of the articulated …

The goal of this paper is to address the problem of\textit {global} point cloud registration
(PCR) ie, finding the optimal alignment between point clouds irrespective of the initial poses …