# Learning 6-DoF Task-oriented Grasp Detection via Implicit Estimation and Visual Affordance

arXiv: https://arxiv.org/abs/2210.08537 · HTML: https://mlsanity.com/paper/2210.08537/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2022.

## Built on by (most cited first, 8 of 8)

- [AffordPose: A Large-scale Dataset of Hand-Object Interactions with Affordance-driven Hand Pose](https://mlsanity.com/paper/2309.08942/) (2023)
- [Learning 6-DoF Fine-grained Grasp Detection Based on Part Affordance Grounding](https://mlsanity.com/paper/2301.11564/) (2023)
- [Text-driven Affordance Learning from Egocentric Vision](https://mlsanity.com/paper/2404.02523/) (2024)
- [Learning Any-View 6DoF Robotic Grasping in Cluttered Scenes via Neural Surface Rendering](https://mlsanity.com/paper/2306.07392/) (2023)
- [PAVLM: Advancing Point Cloud based Affordance Understanding Via Vision-Language Model](https://mlsanity.com/paper/2410.11564/) (2024)
- [Task-Oriented 6-DoF Grasp Pose Detection in Clutters](https://mlsanity.com/paper/2502.16976/) (2025)
- [Visual Imitation Learning of Task-Oriented Object Grasping and Rearrangement](https://mlsanity.com/paper/2403.14000/) (2024)
- [VideoAfford: Grounding 3D Affordance from Human-Object-Interaction Videos via Multimodal Large Language Model](https://mlsanity.com/paper/2602.09638/) (2026)

## Abstract (arXiv)

Currently, task-oriented grasp detection approaches are mostly based on pixel-level affordance detection and semantic segmentation. These pixel-level approaches heavily rely on the accuracy of a 2D affordance mask, and the generated grasp candidates are restricted to a small workspace. To mitigate these limitations, we first construct a novel affordance-based grasp dataset and propose a 6-DoF task-oriented grasp detection framework, which takes the observed object point cloud as input and predicts diverse 6-DoF grasp poses for different tasks. Specifically, our implicit estimation network and visual affordance network in this framework could directly predict coarse grasp candidates, and corresponding 3D affordance heatmap for each potential task, respectively. Furthermore, the grasping scores from coarse grasps are combined with heatmap values to generate more accurate and finer candidates. Our proposed framework shows significant improvements compared to baselines for existing and novel objects on our simulation dataset. Although our framework is trained based on the simulated objects and environment, the final generated grasp candidates can be accurately and stably executed in real robot experiments when the object is randomly placed on a support surface.