# HMT-Grasp: A Hybrid Mamba-Transformer Approach for Robot Grasping in Cluttered Environments

arXiv: https://arxiv.org/abs/2410.03522 · HTML: https://mlsanity.com/paper/2410.03522/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Vision Mamba backbones, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 22 of its 34 references that are in the map.
- First posted on arXiv 4 Oct 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Vision Mamba backbones](https://mlsanity.com/ssm/vision-mamba-backbones/)

## Builds on (most cited first, 22 of 22)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024)
- [Do Vision Transformers See Like Convolutional Neural Networks?](https://mlsanity.com/paper/2108.08810/) (2021)
- [VM-UNet: Vision Mamba UNet for Medical Image Segmentation](https://mlsanity.com/paper/2402.02491/) (2024)
- [SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation](https://mlsanity.com/paper/2401.13560/) (2024)
- [A Survey of the Recent Architectures of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1901.06032/) (2019)
- [Deep Learning for Detecting Robotic Grasps](https://mlsanity.com/paper/1301.3592/) (2013)
- [Jacquard: A Large Scale Dataset for Robotic Grasp Detection](https://mlsanity.com/paper/1803.11469/) (2018)
- [Fast Vision Transformers with HiLo Attention](https://mlsanity.com/paper/2205.13213/) (2022)
- [Real-Time Grasp Detection Using Convolutional Neural Networks](https://mlsanity.com/paper/1412.3128/) (2014)
- [Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network](https://mlsanity.com/paper/1909.04810/) (2019)
- [Robotic Grasp Detection using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1611.08036/) (2016)
- [End-to-end Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation from RGB](https://mlsanity.com/paper/2107.05287/) (2021)
- [Learning Generalizable Vision-Tactile Robotic Grasping Strategy for Deformable Objects via Transformer](https://mlsanity.com/paper/2112.06374/) (2021)
- [When Transformer Meets Robotic Grasping: Exploits Context for Efficient Grasp Detection](https://mlsanity.com/paper/2202.11911/) (2022)
- [Robotic grasp detection based on Transformer](https://mlsanity.com/paper/2205.15112/) (2022)
- [A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-Resolutions](https://mlsanity.com/paper/2209.07459/) (2022)
- [FViT-Grasp: Grasping Objects With Using Fast Vision Transformers](https://mlsanity.com/paper/2311.13986/) (2023)

## Built on by (most cited first, 2 of 2)

- [VCoT-Grasp: Grasp Foundation Models with Visual Chain-of-Thought Reasoning for Language-driven Grasp Generation](https://mlsanity.com/paper/2510.05827/) (2025)
- [PointNet4D: A Lightweight 4D Point Cloud Video Backbone for Online and Offline Perception in Robotic Applications](https://mlsanity.com/paper/2512.01383/) (2025)

## Abstract (arXiv)

Robot grasping, whether handling isolated objects, cluttered items, or stacked objects, plays a critical role in industrial and service applications. However, current visual grasp detection methods based on Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) often struggle to adapt to diverse scenarios, as they tend to emphasize either local or global features exclusively, neglecting complementary cues. In this paper, we propose a novel hybrid Mamba-Transformer approach to address these challenges. Our method improves robotic visual grasping by effectively capturing both global and local information through the integration of Vision Mamba and parallel convolutional-transformer blocks. This hybrid architecture significantly improves adaptability, precision, and flexibility across various robotic tasks. To ensure a fair evaluation, we conducted extensive experiments on the Cornell, Jacquard, and OCID-Grasp datasets, ranging from simple to complex scenarios. Additionally, we performed both simulated and real-world robotic experiments. The results demonstrate that our method not only surpasses state-of-the-art techniques on standard grasping datasets but also delivers strong performance in both simulation and real-world robot applications.