  Affordance detection and pose estimation are of great importance in many
robotic applications. Their combination helps the robot gain an enhanced
manipulation capability, in which the generated pose can facilitate the
corresponding affordance task. Previous methods for affodance-pose joint
learning are limited to a predefined set of affordances, thus limiting the
adaptability of robots in real-world environments. In this paper, we propose a
new method for language-conditioned affordance-pose joint learning in 3D point
clouds. Given a 3D point cloud object, our method detects the affordance region
and generates appropriate 6-DoF poses for any unconstrained affordance label.
Our method consists of an open-vocabulary affordance detection branch and a
language-guided diffusion model that generates 6-DoF poses based on the
affordance text. We also introduce a new high-quality dataset for the task of
language-driven affordance-pose joint learning. Intensive experimental results
demonstrate that our proposed method works effectively on a wide range of
open-vocabulary affordances and outperforms other baselines by a large margin.
In addition, we illustrate the usefulness of our method in real-world robotic
applications. Our code and dataset are publicly available at
https://3DAPNet.github.io
