# Attribute-Based Robotic Grasping with One-Grasp Adaptation

arXiv: https://arxiv.org/abs/2104.02271 · HTML: https://mlsanity.com/paper/2104.02271/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 38 references that are in the map.
- First posted on arXiv 6 Apr 2021.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [A Baseline for Few-Shot Image Classification](https://mlsanity.com/paper/1909.02729/) (2019)
- [Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning](https://mlsanity.com/paper/1803.09956/) (2018)
- [Few-Shot Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.02536/) (2017)
- [Data-Driven Grasp Synthesis - A Survey](https://mlsanity.com/paper/1309.2660/) (2013)
- [Gated-Attention Architectures for Task-Oriented Language Grounding](https://mlsanity.com/paper/1706.07230/) (2017)
- [Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation](https://mlsanity.com/paper/1710.06422/) (2017)
- [Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter](https://mlsanity.com/paper/1903.01588/) (2019)
- [Grasp2Vec: Learning Object Representations from Self-Supervised Grasping](https://mlsanity.com/paper/1811.06964/) (2018)
- [End-to-End Learning of Semantic Grasping](https://mlsanity.com/paper/1707.01932/) (2017)
- [A Deep Learning Approach to Grasping the Invisible](https://mlsanity.com/paper/1909.04840/) (2019)
- [Grounding Language Attributes to Objects using Bayesian Eigenobjects](https://mlsanity.com/paper/1905.13153/) (2019)
- [Learning to Generate 6-DoF Grasp Poses with Reachability Awareness](https://mlsanity.com/paper/1910.06404/) (2019)
- [Learning Visual Affordances with Target-Orientated Deep Q-Network to Grasp Objects by Harnessing Environmental Fixtures](https://mlsanity.com/paper/1910.03781/) (2019)

## Built on by (most cited first, 7 of 7)

- [Graph Inverse Reinforcement Learning from Diverse Videos](https://mlsanity.com/paper/2207.14299/) (2022)
- [Learning 6-DoF Object Poses to Grasp Category-level Objects by Language Instructions](https://mlsanity.com/paper/2205.04028/) (2022)
- [Attribute-Based Robotic Grasping with Data-Efficient Adaptation](https://mlsanity.com/paper/2501.02149/) (2025)
- [WALL-E: Embodied Robotic WAiter Load Lifting with Large Language Model](https://mlsanity.com/paper/2308.15962/) (2023)
- [LLMs for Robotic Object Disambiguation](https://mlsanity.com/paper/2401.03388/) (2024)
- [Category-level Shape Estimation for Densely Cluttered Objects](https://mlsanity.com/paper/2302.11983/) (2023)
- [Remote Task-oriented Grasp Area Teaching By Non-Experts through Interactive Segmentation and Few-Shot Learning](https://mlsanity.com/paper/2303.10195/) (2023)

## Abstract (arXiv)

Robotic grasping is one of the most fundamental robotic manipulation tasks and has been actively studied. However, how to quickly teach a robot to grasp a novel target object in clutter remains challenging. This paper attempts to tackle the challenge by leveraging object attributes that facilitate recognition, grasping, and quick adaptation. In this work, we introduce an end-to-end learning method of attribute-based robotic grasping with one-grasp adaptation capability. Our approach fuses the embeddings of a workspace image and a query text using a gated-attention mechanism and learns to predict instance grasping affordances. Besides, we utilize object persistence before and after grasping to learn a joint metric space of visual and textual attributes. Our model is self-supervised in a simulation that only uses basic objects of various colors and shapes but generalizes to novel objects and real-world scenes. We further demonstrate that our model is capable of adapting to novel objects with only one grasp data and improving instance grasping performance significantly. Experimental results in both simulation and the real world demonstrate that our approach achieves over 80% instance grasping success rate on unknown objects, which outperforms several baselines by large margins.