# AdaGrasp: Learning an Adaptive Gripper-Aware Grasping Policy

arXiv: https://arxiv.org/abs/2011.14206 · HTML: https://mlsanity.com/paper/2011.14206/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2020.

## Built on by (most cited first, 16 of 16)

- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [GenDexGrasp: Generalizable Dexterous Grasping](https://mlsanity.com/paper/2210.00722/) (2022)
- [PartManip: Learning Cross-Category Generalizable Part Manipulation Policy from Point Cloud Observations](https://mlsanity.com/paper/2303.16958/) (2023)
- [GraspGen: A Diffusion-based Framework for 6-DOF Grasping with On-Generator Training](https://mlsanity.com/paper/2507.13097/) (2025)
- [Learning High-DOF Reaching-and-Grasping via Dynamic Representation of Gripper-Object Interaction](https://mlsanity.com/paper/2204.13998/) (2022)
- [Agentic AI Needs a Systems Theory](https://mlsanity.com/paper/2503.00237/) (2025)
- [OXE-AugE: A Large-Scale Robot Augmentation of OXE for Scaling Cross-Embodiment Policy Learning](https://mlsanity.com/paper/2512.13100/) (2025)
- [Model-free Grasping with Multi-Suction Cup Grippers for Robotic Bin Picking](https://mlsanity.com/paper/2307.16488/) (2023)
- [SAGE: Bridging Semantic and Actionable Parts for GEneralizable Manipulation of Articulated Objects](https://mlsanity.com/paper/2312.01307/) (2023)
- [RobotFingerPrint: Unified Gripper Coordinate Space for Multi-Gripper Grasp Synthesis and Transfer](https://mlsanity.com/paper/2409.14519/) (2024)
- [Corner-Grasp: Multi-Action Grasp Detection and Active Gripper Adaptation for Grasping in Cluttered Environments](https://mlsanity.com/paper/2504.01861/) (2025)
- [Adaptive Vision-Language Grasping via Composable Foundation Priors and Generalizable Grasp Synthesis](https://mlsanity.com/paper/2609.04096/) (2026)
- [XGrasp: Gripper-Aware Grasp Detection with Multi-Gripper Data Generation](https://mlsanity.com/paper/2510.11036/) (2025)
- [Beyond Visual Grasping: Benchmarking Complex Grasping from Detection to Execution](https://mlsanity.com/paper/2607.14341/) (2026)
- [GOAG: Generative and Object-Agnostic Grasp Planner for Dexterous Robotic Manipulation](https://mlsanity.com/paper/2608.19759/) (2026)
- [Gripper-aware Vision Language Action Models](https://mlsanity.com/paper/2608.24603/) (2026)

## Abstract (arXiv)

This paper aims to improve robots' versatility and adaptability by allowing them to use a large variety of end-effector tools and quickly adapt to new tools. We propose AdaGrasp, a method to learn a single grasping policy that generalizes to novel grippers. By training on a large collection of grippers, our algorithm is able to acquire generalizable knowledge of how different grippers should be used in various tasks. Given a visual observation of the scene and the gripper, AdaGrasp infers the possible grasp poses and their grasp scores by computing the cross convolution between the shape encodings of the gripper and scene. Intuitively, this cross convolution operation can be considered as an efficient way of exhaustively matching the scene geometry with gripper geometry under different grasp poses (i.e., translations and orientations), where a good "match" of 3D geometry will lead to a successful grasp. We validate our methods in both simulation and real-world environments. Our experiment shows that AdaGrasp significantly outperforms the existing multi-gripper grasping policy method, especially when handling cluttered environments and partial observations. Video is available at https://youtu.be/kknTYTbORfs