# CEDex: Cross-Embodiment Dexterous Grasp Generation at Scale from Human-like Contact Representations

arXiv: https://arxiv.org/abs/2509.24661 · HTML: https://mlsanity.com/paper/2509.24661/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 31 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Builds on (most cited first, 25 of 25)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [GRAB: A Dataset of Whole-Body Human Grasping of Objects](https://mlsanity.com/paper/2008.11200/) (2020)
- [DexYCB: A Benchmark for Capturing Hand Grasping of Objects](https://mlsanity.com/paper/2104.04631/) (2021)
- [Hand-Object Contact Consistency Reasoning for Human Grasps Generation](https://mlsanity.com/paper/2104.03304/) (2021)
- [ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging](https://mlsanity.com/paper/1904.06830/) (2019)
- [ContactOpt: Optimizing Contact to Improve Grasps](https://mlsanity.com/paper/2104.07267/) (2021)
- [DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation](https://mlsanity.com/paper/2210.02697/) (2022)
- [UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy](https://mlsanity.com/paper/2303.00938/) (2023)
- [UniDexGrasp++: Improving Dexterous Grasping Policy Learning via Geometry-aware Curriculum and Iterative Generalist-Specialist Learning](https://mlsanity.com/paper/2304.00464/) (2023)
- [GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learning](https://mlsanity.com/paper/1810.05762/) (2018)
- [LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot Learning](https://mlsanity.com/paper/2309.06440/) (2023)
- [Synthesizing Diverse and Physically Stable Grasps with Arbitrary Hand Structures using Differentiable Force Closure Estimator](https://mlsanity.com/paper/2104.09194/) (2021)
- [GraspXL: Generating Grasping Motions for Diverse Objects at Scale](https://mlsanity.com/paper/2403.19649/) (2024)
- [GenDexGrasp: Generalizable Dexterous Grasping](https://mlsanity.com/paper/2210.00722/) (2022)
- [ContactGen: Generative Contact Modeling for Grasp Generation](https://mlsanity.com/paper/2310.03740/) (2023)
- [D(R,O) Grasp: A Unified Representation of Robot and Object Interaction for Cross-Embodiment Dexterous Grasping](https://mlsanity.com/paper/2410.01702/) (2024)
- [Deep Differentiable Grasp Planner for High-DOF Grippers](https://mlsanity.com/paper/2002.01530/) (2020)
- [RealDex: Towards Human-like Grasping for Robotic Dexterous Hand](https://mlsanity.com/paper/2402.13853/) (2024)
- [DexGrasp Anything: Towards Universal Robotic Dexterous Grasping with Physics Awareness](https://mlsanity.com/paper/2503.08257/) (2025)
- [Geometry Matching for Multi-Embodiment Grasping](https://mlsanity.com/paper/2312.03864/) (2023)
- [Multi-GraspLLM: A Multimodal LLM for Multi-Hand Semantic Guided Grasp Generation](https://mlsanity.com/paper/2412.08468/) (2024)
- [GeoMatch++: Morphology Conditioned Geometry Matching for Multi-Embodiment Grasping](https://mlsanity.com/paper/2412.18998/) (2024)
- [Learning Cross-hand Policies for High-DOF Reaching and Grasping](https://mlsanity.com/paper/2404.09150/) (2024)

## Built on by (most cited first, 9 of 9)

- [Cross-Hand Latent Representation for Vision-Language-Action Models](https://mlsanity.com/paper/2603.10158/) (2026)
- [HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments](https://mlsanity.com/paper/2604.14944/) (2026)
- [UniMorphGrasp: Diffusion Model with Morphology-Awareness for Cross-Embodiment Dexterous Grasp Generation](https://mlsanity.com/paper/2602.00915/) (2026)
- [HO-Flow: Generalizable Hand-Object Interaction Generation with Latent Flow Matching](https://mlsanity.com/paper/2604.10836/) (2026)
- [KITE: Decoupling Kinematics and Interaction for Zero-Shot Cross-Embodiment Manipulation](https://mlsanity.com/paper/2606.22113/) (2026)
- [Adaptive Vision-Language Grasping via Composable Foundation Priors and Generalizable Grasp Synthesis](https://mlsanity.com/paper/2609.04096/) (2026)
- [VTLoc: Learning-based Tactile Contact Localization in Visual Point Clouds](https://mlsanity.com/paper/2607.16146/) (2026)
- [MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping](https://mlsanity.com/paper/2608.02014/) (2026)
- [Gripper-aware Vision Language Action Models](https://mlsanity.com/paper/2608.24603/) (2026)

## Abstract (arXiv)

Cross-embodiment dexterous grasp synthesis refers to adaptively generating and optimizing grasps for various robotic hands with different morphologies. This capability is crucial for achieving versatile robotic manipulation in diverse environments and requires substantial amounts of reliable and diverse grasp data for effective model training and robust generalization. However, existing approaches either rely on physics-based optimization that lacks human-like kinematic understanding or require extensive manual data collection processes that are limited to anthropomorphic structures. In this paper, we propose CEDex, a novel cross-embodiment dexterous grasp synthesis method at scale that bridges human grasping kinematics and robot kinematics by aligning robot kinematic models with generated human-like contact representations. Given an object's point cloud and an arbitrary robotic hand model, CEDex first generates human-like contact representations using a Conditional Variational Auto-encoder pretrained on human contact data. It then performs kinematic human contact alignment through topological merging to consolidate multiple human hand parts into unified robot components, followed by a signed distance field-based grasp optimization with physics-aware constraints. Using CEDex, we construct the largest cross-embodiment grasp dataset to date, comprising 500K objects across four gripper types with 20M total grasps. Extensive experiments show that CEDex outperforms state-of-the-art approaches and our dataset benefits cross-embodiment grasp learning with high-quality diverse grasps.