# Continuous Wrist Control on the Hannes Prosthesis: a Vision-based Shared Autonomy Framework

arXiv: https://arxiv.org/abs/2502.17265 · HTML: https://mlsanity.com/paper/2502.17265/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 24 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Exploring Plain Vision Transformer Backbones for Object Detection](https://mlsanity.com/paper/2203.16527/) (2022)
- [AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection](https://mlsanity.com/paper/1709.07326/) (2017)
- [Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocols](https://mlsanity.com/paper/1502.03143/) (2015)
- [Deep Learning Approaches to Grasp Synthesis: A Review](https://mlsanity.com/paper/2207.02556/) (2022)
- [Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared Control on the Hannes Prosthesis](https://mlsanity.com/paper/2203.09812/) (2022)
- [Semi-autonomous Prosthesis Control Using Minimal Depth Information and Vibrotactile Feedback](https://mlsanity.com/paper/2210.00541/) (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)
- [ReGlove: A Soft Pneumatic Glove for Activities of Daily Living Assistance via Wrist-Mounted Vision](https://mlsanity.com/paper/2512.11824/) (2025)

## Abstract (arXiv)

Most control techniques for prosthetic grasping focus on dexterous fingers control, but overlook the wrist motion. This forces the user to perform compensatory movements with the elbow, shoulder and hip to adapt the wrist for grasping. We propose a computer vision-based system that leverages the collaboration between the user and an automatic system in a shared autonomy framework, to perform continuous control of the wrist degrees of freedom in a prosthetic arm, promoting a more natural approach-to-grasp motion. Our pipeline allows to seamlessly control the prosthetic wrist to follow the target object and finally orient it for grasping according to the user intent. We assess the effectiveness of each system component through quantitative analysis and finally deploy our method on the Hannes prosthetic arm. Code and videos: https://hsp-iit.github.io/hannes-wrist-control.