# A Collision-Aware Cable Grasping Method in Cluttered Environment

arXiv: https://arxiv.org/abs/2402.14498 · HTML: https://mlsanity.com/paper/2402.14498/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 31 references that are in the map.
- First posted on arXiv 22 Feb 2024.

## Builds on (most cited first, 19 of 19)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics](https://mlsanity.com/paper/1703.09312/) (2017)
- [Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes](https://mlsanity.com/paper/2103.14127/) (2021)
- [Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning](https://mlsanity.com/paper/1803.09956/) (2018)
- [Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching](https://mlsanity.com/paper/1710.01330/) (2017)
- [Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach](https://mlsanity.com/paper/1804.05172/) (2018)
- [ACRONYM: A Large-Scale Grasp Dataset Based on Simulation](https://mlsanity.com/paper/2011.09584/) (2020)
- [PointNetGPD: Detecting Grasp Configurations from Point Sets](https://mlsanity.com/paper/1809.06267/) (2018)
- [Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks](https://mlsanity.com/paper/2012.03385/) (2020)
- [EGAD! an Evolved Grasping Analysis Dataset for diversity and reproducibility in robotic manipulation](https://mlsanity.com/paper/2003.01314/) (2020)
- [Cable Manipulation with a Tactile-Reactive Gripper](https://mlsanity.com/paper/1910.02860/) (2019)
- [Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor](https://mlsanity.com/paper/1910.04854/) (2019)
- [MetaGraspNet: A Large-Scale Benchmark Dataset for Scene-Aware Ambidextrous Bin Picking via Physics-based Metaverse Synthesis](https://mlsanity.com/paper/2208.03963/) (2022)
- [6D Robotic Assembly Based on RGB-only Object Pose Estimation](https://mlsanity.com/paper/2208.12986/) (2022)
- [Real-Time 6D Object Pose Estimation on CPU](https://mlsanity.com/paper/1811.08588/) (2018)
- [MetaGrasp: Data Efficient Grasping by Affordance Interpreter Network](https://mlsanity.com/paper/1902.06554/) (2019)
- [Primitive Shape Recognition for Object Grasping](https://mlsanity.com/paper/2201.00956/) (2022)
- [Learning Efficient Policies for Picking Entangled Wire Harnesses: An Approach to Industrial Bin Picking](https://mlsanity.com/paper/2112.05941/) (2021)
- [Towards Precise Model-free Robotic Grasping with Sim-to-Real Transfer Learning](https://mlsanity.com/paper/2301.12249/) (2023)

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

- [ContactDexNet: Multi-fingered Robotic Hand Grasping in Cluttered Environments through Hand-object Contact Semantic Mapping](https://mlsanity.com/paper/2404.08844/) (2024)
- [FUNCanon: Learning Pose-Aware Action Primitives via Functional Object Canonicalization for Generalizable Robotic Manipulation](https://mlsanity.com/paper/2509.19102/) (2025)

## Abstract (arXiv)

We introduce a Cable Grasping-Convolutional Neural Network designed to facilitate robust cable grasping in cluttered environments. Utilizing physics simulations, we generate an extensive dataset that mimics the intricacies of cable grasping, factoring in potential collisions between cables and robotic grippers. We employ the Approximate Convex Decomposition technique to dissect the non-convex cable model, with grasp quality autonomously labeled based on simulated grasping attempts. The CG-CNN is refined using this simulated dataset and enhanced through domain randomization techniques. Subsequently, the trained model predicts grasp quality, guiding the optimal grasp pose to the robot controller for execution. Grasping efficacy is assessed across both synthetic and real-world settings. Given our model implicit collision sensitivity, we achieved commendable success rates of 92.3% for known cables and 88.4% for unknown cables, surpassing contemporary state-of-the-art approaches. Supplementary materials can be found at https://leizhang-public.github.io/cg-cnn/ .