# Bring Your Own Grasp Generator: Leveraging Robot Grasp Generation for Prosthetic Grasping

arXiv: https://arxiv.org/abs/2503.00466 · HTML: https://mlsanity.com/paper/2503.00466/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 1 Mar 2025.

## Builds on (most cited first, 16 of 16)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras](https://mlsanity.com/paper/2108.10869/) (2021)
- [6-DOF GraspNet: Variational Grasp Generation for Object Manipulation](https://mlsanity.com/paper/1905.10520/) (2019)
- [Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes](https://mlsanity.com/paper/2103.14127/) (2021)
- [Deep Patch Visual Odometry](https://mlsanity.com/paper/2208.04726/) (2022)
- [Grasping Field: Learning Implicit Representations for Human Grasps](https://mlsanity.com/paper/2008.04451/) (2020)
- [Unseen Object Instance Segmentation for Robotic Environments](https://mlsanity.com/paper/2007.08073/) (2020)
- [6-DOF Grasping for Target-driven Object Manipulation in Clutter](https://mlsanity.com/paper/1912.03628/) (2019)
- [GenDexGrasp: Generalizable Dexterous Grasping](https://mlsanity.com/paper/2210.00722/) (2022)
- [End-to-End Learning to Grasp via Sampling from Object Point Clouds](https://mlsanity.com/paper/2203.05585/) (2022)
- [EfficientGrasp: A Unified Data-Efficient Learning to Grasp Method for Multi-fingered Robot Hands](https://mlsanity.com/paper/2206.15159/) (2022)
- [Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared Control on the Hannes Prosthesis](https://mlsanity.com/paper/2203.09812/) (2022)

## Built on by (most cited first, 2 of 2)

- [HannesImitation: Grasping with the Hannes Prosthetic Hand via Imitation Learning](https://mlsanity.com/paper/2508.00491/) (2025)
- [Simulation-Driven Imitation Learning for Biosignals-Free Shared-Autonomy Prosthetic Grasping](https://mlsanity.com/paper/2606.07389/) (2026)

## Abstract (arXiv)

One of the most important research challenges in upper-limb prosthetics is enhancing the user-prosthesis communication to closely resemble the experience of a natural limb. As prosthetic devices become more complex, users often struggle to control the additional degrees of freedom. In this context, leveraging shared-autonomy principles can significantly improve the usability of these systems. In this paper, we present a novel eye-in-hand prosthetic grasping system that follows these principles. Our system initiates the approach-to-grasp action based on user's command and automatically configures the DoFs of a prosthetic hand. First, it reconstructs the 3D geometry of the target object without the need of a depth camera. Then, it tracks the hand motion during the approach-to-grasp action and finally selects a candidate grasp configuration according to user's intentions. We deploy our system on the Hannes prosthetic hand and test it on able-bodied subjects and amputees to validate its effectiveness. We compare it with a multi-DoF prosthetic control baseline and find that our method enables faster grasps, while simplifying the user experience. Code and demo videos are available online at https://hsp-iit.github.io/byogg/.