# Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach

arXiv: https://arxiv.org/abs/1804.05172 · HTML: https://mlsanity.com/paper/1804.05172/

## Key facts

- Cited by 120 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2018.

## Built on by (most cited first, 30 of 120)

- [How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned](https://mlsanity.com/paper/2102.02915/) (2021)
- [ContactOpt: Optimizing Contact to Improve Grasps](https://mlsanity.com/paper/2104.07267/) (2021)
- [Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisation](https://mlsanity.com/paper/2106.12534/) (2021)
- [Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations](https://mlsanity.com/paper/2104.01542/) (2021)
- [Q-attention: Enabling Efficient Learning for Vision-based Robotic Manipulation](https://mlsanity.com/paper/2105.14829/) (2021)
- [Waypoint-Based Imitation Learning for Robotic Manipulation](https://mlsanity.com/paper/2307.14326/) (2023)
- [NeRF in the Palm of Your Hand: Corrective Augmentation for Robotics via Novel-View Synthesis](https://mlsanity.com/paper/2301.08556/) (2023)
- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [Graspness Discovery in Clutters for Fast and Accurate Grasp Detection](https://mlsanity.com/paper/2406.11142/) (2024)
- [Equivariant Transporter Network](https://mlsanity.com/paper/2202.09400/) (2022)
- [End-to-end Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation from RGB](https://mlsanity.com/paper/2107.05287/) (2021)
- [DexGraspNet 2.0: Learning Generative Dexterous Grasping in Large-scale Synthetic Cluttered Scenes](https://mlsanity.com/paper/2410.23004/) (2024)
- [IFOR: Iterative Flow Minimization for Robotic Object Rearrangement](https://mlsanity.com/paper/2202.00732/) (2022)
- [RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD Images](https://mlsanity.com/paper/2103.02184/) (2021)
- [Edge Grasp Network: A Graph-Based SE(3)-invariant Approach to Grasp Detection](https://mlsanity.com/paper/2211.00191/) (2022)
- [Deep Generative Models in Robotics: A Survey on Learning from Multimodal Demonstrations](https://mlsanity.com/paper/2408.04380/) (2024)
- [A Survey of Embodied Learning for Object-Centric Robotic Manipulation](https://mlsanity.com/paper/2408.11537/) (2024)
- [Learning Continuous Grasping Function with a Dexterous Hand from Human Demonstrations](https://mlsanity.com/paper/2207.05053/) (2022)
- [Learning Human-to-Robot Handovers from Point Clouds](https://mlsanity.com/paper/2303.17592/) (2023)
- [Grasp-Anything: Large-scale Grasp Dataset from Foundation Models](https://mlsanity.com/paper/2309.09818/) (2023)
- [Fourier Transporter: Bi-Equivariant Robotic Manipulation in 3D](https://mlsanity.com/paper/2401.12046/) (2024)
- [Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview](https://mlsanity.com/paper/2105.14291/) (2021)
- [DDGC: Generative Deep Dexterous Grasping in Clutter](https://mlsanity.com/paper/2103.04783/) (2021)
- [Integrating Action Knowledge and LLMs for Task Planning and Situation Handling in Open Worlds](https://mlsanity.com/paper/2305.17590/) (2023)
- [Language-driven Grasp Detection](https://mlsanity.com/paper/2406.09489/) (2024)
- [Learning High-DOF Reaching-and-Grasping via Dynamic Representation of Gripper-Object Interaction](https://mlsanity.com/paper/2204.13998/) (2022)
- [Grounding Classical Task Planners via Vision-Language Models](https://mlsanity.com/paper/2304.08587/) (2023)
- [An Economic Framework for 6-DoF Grasp Detection](https://mlsanity.com/paper/2407.08366/) (2024)
- [Language-guided Robot Grasping: CLIP-based Referring Grasp Synthesis in Clutter](https://mlsanity.com/paper/2311.05779/) (2023)
- [Towards Scale Balanced 6-DoF Grasp Detection in Cluttered Scenes](https://mlsanity.com/paper/2212.05275/) (2022)

## Abstract (arXiv)

This paper presents a real-time, object-independent grasp synthesis method which can be used for closed-loop grasping. Our proposed Generative Grasping Convolutional Neural Network (GG-CNN) predicts the quality and pose of grasps at every pixel. This one-to-one mapping from a depth image overcomes limitations of current deep-learning grasping techniques by avoiding discrete sampling of grasp candidates and long computation times. Additionally, our GG-CNN is orders of magnitude smaller while detecting stable grasps with equivalent performance to current state-of-the-art techniques. The light-weight and single-pass generative nature of our GG-CNN allows for closed-loop control at up to 50Hz, enabling accurate grasping in non-static environments where objects move and in the presence of robot control inaccuracies. In our real-world tests, we achieve an 83% grasp success rate on a set of previously unseen objects with adversarial geometry and 88% on a set of household objects that are moved during the grasp attempt. We also achieve 81% accuracy when grasping in dynamic clutter.