# Anthropomorphic Grasping with Neural Object Shape Completion

arXiv: https://arxiv.org/abs/2311.02510 · HTML: https://mlsanity.com/paper/2311.02510/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 43 references that are in the map.
- First posted on arXiv 4 Nov 2023.

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

- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [PointRend: Image Segmentation as Rendering](https://mlsanity.com/paper/1912.08193/) (2019)
- [GRAB: A Dataset of Whole-Body Human Grasping of Objects](https://mlsanity.com/paper/2008.11200/) (2020)
- [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)
- [Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hours](https://mlsanity.com/paper/1509.06825/) (2015)
- [Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation](https://mlsanity.com/paper/2112.05124/) (2021)
- [Hand-Object Contact Consistency Reasoning for Human Grasps Generation](https://mlsanity.com/paper/2104.03304/) (2021)
- [Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations](https://mlsanity.com/paper/2104.01542/) (2021)
- [MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects](https://mlsanity.com/paper/1804.09194/) (2018)
- [Shape Completion Enabled Robotic Grasping](https://mlsanity.com/paper/1609.08546/) (2016)
- [MID-Fusion: Octree-based Object-Level Multi-Instance Dynamic SLAM](https://mlsanity.com/paper/1812.07976/) (2018)
- [S4G: Amodal Single-view Single-Shot SE(3) Grasp Detection in Cluttered Scenes](https://mlsanity.com/paper/1910.14218/) (2019)
- [CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation](https://mlsanity.com/paper/2109.09163/) (2021)
- [DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM](https://mlsanity.com/paper/2010.07820/) (2020)
- [Learning Continuous 3D Reconstructions for Geometrically Aware Grasping](https://mlsanity.com/paper/1910.00983/) (2019)
- [Generative Category-Level Shape and Pose Estimation with Semantic Primitives](https://mlsanity.com/paper/2210.01112/) (2022)
- [Human Grasp Classification for Reactive Human-to-Robot Handovers](https://mlsanity.com/paper/2003.06000/) (2020)
- [TransGrasp: Grasp Pose Estimation of a Category of Objects by Transferring Grasps from Only One Labeled Instance](https://mlsanity.com/paper/2207.07861/) (2022)
- [Learning to Complete Object Shapes for Object-level Mapping in Dynamic Scenes](https://mlsanity.com/paper/2208.05067/) (2022)
- [Generalized Anthropomorphic Functional Grasping with Minimal Demonstrations](https://mlsanity.com/paper/2303.17808/) (2023)

## Built on by (most cited first, 4 of 4)

- [Interactive Imitation Learning for Dexterous Robotic Manipulation: Challenges and Perspectives -- A Survey](https://mlsanity.com/paper/2506.00098/) (2025)
- [Visual Imitation Learning of Task-Oriented Object Grasping and Rearrangement](https://mlsanity.com/paper/2403.14000/) (2024)
- [You Only Scan Once: A Dynamic Scene Reconstruction Pipeline for 6-DoF Robotic Grasping of Novel Objects](https://mlsanity.com/paper/2404.03462/) (2024)
- [Robust Bayesian Scene Reconstruction with Retrieval-Augmented Priors for Precise Grasping and Planning](https://mlsanity.com/paper/2411.19461/) (2024)

## Abstract (arXiv)

The progressive prevalence of robots in human-suited environments has given rise to a myriad of object manipulation techniques, in which dexterity plays a paramount role. It is well-established that humans exhibit extraordinary dexterity when handling objects. Such dexterity seems to derive from a robust understanding of object properties (such as weight, size, and shape), as well as a remarkable capacity to interact with them. Hand postures commonly demonstrate the influence of specific regions on objects that need to be grasped, especially when objects are partially visible. In this work, we leverage human-like object understanding by reconstructing and completing their full geometry from partial observations, and manipulating them using a 7-DoF anthropomorphic robot hand. Our approach has significantly improved the grasping success rates of baselines with only partial reconstruction by nearly 30% and achieved over 150 successful grasps with three different object categories. This demonstrates our approach's consistent ability to predict and execute grasping postures based on the completed object shapes from various directions and positions in real-world scenarios. Our work opens up new possibilities for enhancing robotic applications that require precise grasping and manipulation skills of real-world reconstructed objects.