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

Imitation learning aims to extract high-performance policies from logged demonstrations of
expert behavior. It is common to frame imitation learning as a supervised learning problem …

In the era of digitalization, the interaction between humans and machines, particularly in
Natural Language Processing, has gained crucial importance. This study focuses on …

Rearrangement tasks have been identified as a crucial challenge for intelligent robotic
manipulation, but few methods allow for precise construction of unseen structures. We …

Object rearranging is one of the most common deformable manipulation tasks, where the
robot needs to rearrange a deformable object into a goal configuration. Previous studies …

Precise pick-and-place is essential in robotic applications. To this end, we define an exact
training method and an iterative inference method that improve pick-and-place precision …

Goal-conditioned rearrangement of deformable objects (eg straightening a rope and folding
a cloth) is one of the most common deformable manipulation tasks, where the robot needs to …

The challenges inherent to long-horizon tasks in robotics persist due to the typical inefficient
exploration and sparse rewards in traditional reinforcement learning approaches. To …

In this paper, we propose a novel coarse-to-fine continuous pose diffusion method to
enhance the precision of pick-and-place operations within robotic manipulation tasks …

In this work, we focus on addressing the long-horizon manipulation tasks in densely
cluttered scenes. Such tasks require policies to effectively manage severe occlusions …