We address the problem of generating realistic 3D human-object interactions (HOIs) driven
by textual prompts. Instead of a single model, our key insight is to take a modular design and …

Abstract We introduce One-shot Open Affordance Learning (OOAL) where a model is trained
with just one example per base object category but is expected to identify novel objects and …

DoF grasp detection has been a fundamental and challenging problem in robotic vision.
While previous works have focused on ensuring grasp stability, they often do not consider …

In this paper, we propose LAN-grasp, a novel approach towards more appropriate semantic
grasping. We use foundation models to provide the robot with a deeper understanding of the …

Affordance detection and pose estimation are of great importance in many robotic
applications. Their combination helps the robot gain an enhanced manipulation capability …

Foundation models such as ChatGPT have made significant strides in robotic tasks due to
their universal representation of real-world domains. In this paper, we leverage foundation …

Robotic grasping is a fundamental ability for a robot to interact with the environment. Current
methods focus on how to obtain a stable and reliable grasping pose in object level, while …

Affordance detection presents intricate challenges and has a wide range of robotic
applications. Previous works have faced limitations such as the complexities of 3D object …

The household rearrangement task involves spotting misplaced objects in a scene and
accommodate them with proper places. It depends both on common-sense knowledge on …

Affordance, defined as the potential actions that an object offers, is crucial for robotic
manipulation tasks. A deep understanding of affordance can lead to more intelligent AI …