# MachaGrasp: Morphology-Aware Cross-Embodiment Dexterous Hand Articulation Generation for Grasping

arXiv: https://arxiv.org/abs/2510.06068 · HTML: https://mlsanity.com/paper/2510.06068/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 26 references that are in the map.
- First posted on arXiv 7 Oct 2025.

## Builds on (most cited first, 21 of 21)

- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [A Point Set Generation Network for 3D Object Reconstruction from a Single Image](https://mlsanity.com/paper/1612.00603/) (2016)
- [6-DOF GraspNet: Variational Grasp Generation for Object Manipulation](https://mlsanity.com/paper/1905.10520/) (2019)
- [DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation](https://mlsanity.com/paper/2210.02697/) (2022)
- [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)
- [Synthesizing Diverse and Physically Stable Grasps with Arbitrary Hand Structures using Differentiable Force Closure Estimator](https://mlsanity.com/paper/2104.09194/) (2021)
- [GenDexGrasp: Generalizable Dexterous Grasping](https://mlsanity.com/paper/2210.00722/) (2022)
- [Grasp'D: Differentiable Contact-rich Grasp Synthesis for Multi-fingered Hands](https://mlsanity.com/paper/2208.12250/) (2022)
- [D(R,O) Grasp: A Unified Representation of Robot and Object Interaction for Cross-Embodiment Dexterous Grasping](https://mlsanity.com/paper/2410.01702/) (2024)
- [Fast-Grasp'D: Dexterous Multi-finger Grasp Generation Through Differentiable Simulation](https://mlsanity.com/paper/2306.08132/) (2023)
- [GET-Zero: Graph Embodiment Transformer for Zero-shot Embodiment Generalization](https://mlsanity.com/paper/2407.15002/) (2024)
- [Cross-Embodiment Dexterous Grasping with Reinforcement Learning](https://mlsanity.com/paper/2410.02479/) (2024)
- [RobustDexGrasp: Robust Dexterous Grasping of General Objects](https://mlsanity.com/paper/2504.05287/) (2025)
- [DexRepNet: Learning Dexterous Robotic Grasping Network with Geometric and Spatial Hand-Object Representations](https://mlsanity.com/paper/2303.09806/) (2023)
- [MultiGripperGrasp: A Dataset for Robotic Grasping from Parallel Jaw Grippers to Dexterous Hands](https://mlsanity.com/paper/2403.09841/) (2024)
- [AnyDexGrasp: General Dexterous Grasping for Different Hands with Human-level Learning Efficiency](https://mlsanity.com/paper/2502.16420/) (2025)
- [Geometry Matching for Multi-Embodiment Grasping](https://mlsanity.com/paper/2312.03864/) (2023)
- [UniGraspTransformer: Simplified Policy Distillation for Scalable Dexterous Robotic Grasping](https://mlsanity.com/paper/2412.02699/) (2024)
- [Refining 6-DoF Grasps with Context-Specific Classifiers](https://mlsanity.com/paper/2308.06928/) (2023)

## Built on by (most cited first, 3 of 3)

- [UniMorphGrasp: Diffusion Model with Morphology-Awareness for Cross-Embodiment Dexterous Grasp Generation](https://mlsanity.com/paper/2602.00915/) (2026)
- [KITE: Decoupling Kinematics and Interaction for Zero-Shot Cross-Embodiment Manipulation](https://mlsanity.com/paper/2606.22113/) (2026)
- [MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping](https://mlsanity.com/paper/2608.02014/) (2026)

## Abstract (arXiv)

Dexterous grasping with multi-fingered hands remains challenging due to high-dimensional articulations and the cost of optimization-based pipelines. Existing end-to-end methods require training on large-scale datasets for specific hands, limiting their ability to generalize across different embodiments. We propose MachaGrasp, an eigengrasp-based, end-to-end framework for cross-embodiment grasp generation. From a hand's morphology description, we derive a morphology embedding and an eigengrasp set. Conditioned on these, together with the object point cloud and wrist pose, an amplitude predictor regresses articulation coefficients in a low-dimensional space, which are decoded into full joint articulations. Articulation learning is supervised with a Kinematic-Aware Articulation Loss (KAL) that emphasizes fingertip-relevant motions and injects morphology-specific structure. In simulation on unseen objects across three dexterous hands, MachaGrasp attains a 91.9% average grasp success rate with less than 0.4 seconds inference per grasp. With few-shot adaptation to an unseen hand, it achieves 85.6% success on unseen objects in simulation, and real-world experiments on this few-shot-generalized hand achieve an 87% success rate. The code and additional materials are available on our project website https://connor-zh.github.io/MachaGrasp.