# Language-Conditioned Affordance-Pose Detection in 3D Point Clouds

arXiv: https://arxiv.org/abs/2309.10911 · HTML: https://mlsanity.com/paper/2309.10911/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2023.

## Built on by (most cited first, 16 of 16)

- [Diffusion Models for Robotic Manipulation: A Survey](https://mlsanity.com/paper/2504.08438/) (2025)
- [Language-Driven 6-DoF Grasp Detection Using Negative Prompt Guidance](https://mlsanity.com/paper/2407.13842/) (2024)
- [ZEETAD: Adapting Pretrained Vision-Language Model for Zero-Shot End-to-End Temporal Action Detection](https://mlsanity.com/paper/2311.00729/) (2023)
- [Language-driven Scene Synthesis using Multi-conditional Diffusion Model](https://mlsanity.com/paper/2310.15948/) (2023)
- [Text2Grasp: Grasp synthesis by text prompts of object grasping parts](https://mlsanity.com/paper/2404.15189/) (2024)
- [AffordVLA: Injecting Affordance Representations into Vision-Language-Action Models via Implicit Feature Alignment](https://mlsanity.com/paper/2605.17517/) (2026)
- [PAVLM: Advancing Point Cloud based Affordance Understanding Via Vision-Language Model](https://mlsanity.com/paper/2410.11564/) (2024)
- [Multi-Keypoint Affordance Representation for Functional Dexterous Grasping](https://mlsanity.com/paper/2502.20018/) (2025)
- [From 2D to 3D Cognition: A Brief Survey of General World Models](https://mlsanity.com/paper/2506.20134/) (2025)
- [FunHOI: Annotation-Free 3D Hand-Object Interaction Generation via Functional Text Guidance](https://mlsanity.com/paper/2502.20805/) (2025)
- [GraspCoT: Integrating Physical Property Reasoning for 6-DoF Grasping under Flexible Language Instructions](https://mlsanity.com/paper/2503.16013/) (2025)
- [MBPU: A Plug-and-Play State Space Model for Point Cloud Upsamping with Fast Point Rendering](https://mlsanity.com/paper/2410.15941/) (2024)
- [H2OFlow: Grounding Human-Object Affordances with 3D Generative Models and Dense Diffused Flows](https://mlsanity.com/paper/2510.21769/) (2025)
- [Affostruction: 3D Affordance Grounding with Generative Reconstruction](https://mlsanity.com/paper/2601.09211/) (2026)
- [ConGraspXL: Controllable Constraint-Conditioned Dexterous Grasping Motion Synthesis](https://mlsanity.com/paper/2609.16319/) (2026)
- [DAP: Diffusion-based Affordance Prediction for Multi-modality Storage](https://mlsanity.com/paper/2409.00499/) (2024)

## Abstract (arXiv)

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