# Attribute-Based Robotic Grasping with Data-Efficient Adaptation

arXiv: https://arxiv.org/abs/2501.02149 · HTML: https://mlsanity.com/paper/2501.02149/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 59 references that are in the map.
- First posted on arXiv 4 Jan 2025.

## Builds on (most cited first, 24 of 24)

- [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)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [A Baseline for Few-Shot Image Classification](https://mlsanity.com/paper/1909.02729/) (2019)
- [CAD2RL: Real Single-Image Flight without a Single Real Image](https://mlsanity.com/paper/1611.04201/) (2016)
- [Active Domain Randomization](https://mlsanity.com/paper/1904.04762/) (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)
- [Domain Randomization and Generative Models for Robotic Grasping](https://mlsanity.com/paper/1710.06425/) (2017)
- [Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter](https://mlsanity.com/paper/1903.01588/) (2019)
- [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)
- [Revisiting Data Augmentation for Rotational Invariance in Convolutional Neural Networks](https://mlsanity.com/paper/2310.08429/) (2023)
- [M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning](https://mlsanity.com/paper/1807.02552/) (2018)
- [Self-Supervised Interactive Object Segmentation Through a Singulation-and-Grasping Approach](https://mlsanity.com/paper/2207.09314/) (2022)
- [Learning to Generate 6-DoF Grasp Poses with Reachability Awareness](https://mlsanity.com/paper/1910.06404/) (2019)
- [Attribute-Based Robotic Grasping with One-Grasp Adaptation](https://mlsanity.com/paper/2104.02271/) (2021)

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

- [CoC-VLA: Delving into Adversarial Domain Transfer for Explainable Autonomous Driving via Chain-of-Causality Visual-Language-Action Model](https://mlsanity.com/paper/2511.19914/) (2025)
- [MK-Pose: Category-Level Object Pose Estimation via Multimodal-Based Keypoint Learning](https://mlsanity.com/paper/2507.06662/) (2025)
- [Efficient Alignment of Unconditioned Action Prior for Language-conditioned Pick and Place in Clutter](https://mlsanity.com/paper/2503.09423/) (2025)
- [VCoT-Grasp: Grasp Foundation Models with Visual Chain-of-Thought Reasoning for Language-driven Grasp Generation](https://mlsanity.com/paper/2510.05827/) (2025)
- [Learning Dual-Arm Coordination for Grasping Large Flat Objects](https://mlsanity.com/paper/2504.03500/) (2025)
- [Attribute-based Object Grounding and Robot Grasp Detection with Spatial Reasoning](https://mlsanity.com/paper/2509.08126/) (2025)
- [StateVLM: A State-Aware Vision-Language Model for Robotic Affordance Reasoning](https://mlsanity.com/paper/2605.03927/) (2026)
- [Continual Learning for 6-DoF Grasp Synthesis via Experience and Demonstrations](https://mlsanity.com/paper/2610.01301/) (2026)

## Abstract (arXiv)

Robotic grasping is one of the most fundamental robotic manipulation tasks and has been the subject of extensive research. However, swiftly teaching a robot to grasp a novel target object in clutter remains challenging. This paper attempts to address the challenge by leveraging object attributes that facilitate recognition, grasping, and rapid adaptation to new domains. In this work, we present an end-to-end encoder-decoder network to learn attribute-based robotic grasping with data-efficient adaptation capability. We first pre-train the end-to-end model with a variety of basic objects to learn generic attribute representation for recognition and grasping. 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. To train the joint embedding space of visual and textual attributes, the robot utilizes object persistence before and after grasping. Our model is self-supervised in a simulation that only uses basic objects of various colors and shapes but generalizes to novel objects in new environments. To further facilitate generalization, we propose two adaptation methods, adversarial adaption and one-grasp adaptation. Adversarial adaptation regulates the image encoder using augmented data of unlabeled images, whereas one-grasp adaptation updates the overall end-to-end model using augmented data from one grasp trial. Both adaptation methods are data-efficient and considerably improve instance grasping performance. Experimental results in both simulation and the real world demonstrate that our approach achieves over 81% instance grasping success rate on unknown objects, which outperforms several baselines by large margins.