We marry diffusion policies and 3D scene representations for robot manipulation. Diffusion
policies learn the action distribution conditioned on the robot and environment state using …

This letter presents a differential geometric control approach that leverages SE (3) group
invariance and equivariance to increase transferability in learning robot manipulation tasks …

We introduce DexDiffuser, a novel dexterous grasping method that generates, evaluates,
and refines grasps on partial object point clouds. DexDiffuser includes the conditional …

Diffusion models have risen as a powerful tool in robotics due to their flexibility and multi-
modality. While some of these methods effectively address complex problems, they often …

This paper presents a differential geometric control approach that leverages SE (3) group
invariance and equivariance to increase transferability in learning robot manipulation tasks …

We present RiEMann, an end-to-end near Real-time SE (3)-Equivariant Robot Manipulation
imitation learning framework from scene point cloud input. Compared to previous methods …

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 …

In autonomous driving, precise perception of surrounding objects is crucial for ensuring
safety. To this end, many advanced object detection models have been developed using …

Predicting 3D human motion is a complex task, owing to the unpredictable nature of human
movements. The influx of deep learning innovations and the availability of extensive …

While grasp detection is an important part of any robotic manipulation pipeline, reliable and
accurate grasp detection in $ SE (3) $ remains a research challenge. Many robotics …