# Towards Biosignals-Free Autonomous Prosthetic Hand Control via Imitation Learning

arXiv: https://arxiv.org/abs/2506.08795 · HTML: https://mlsanity.com/paper/2506.08795/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 67 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [Rethinking Positional Encoding in Language Pre-training](https://mlsanity.com/paper/2006.15595/) (2020)
- [DexPilot: Vision Based Teleoperation of Dexterous Robotic Hand-Arm System](https://mlsanity.com/paper/1910.03135/) (2019)
- [RobustDexGrasp: Robust Dexterous Grasping of General Objects](https://mlsanity.com/paper/2504.05287/) (2025)
- [Multimodal Fusion of EMG and Vision for Human Grasp Intent Inference in Prosthetic Hand Control](https://mlsanity.com/paper/2104.03893/) (2021)
- [A Powered Prosthetic Hand with Vision System for Enhancing the Anthropopathic Grasp](https://mlsanity.com/paper/2412.07105/) (2024)
- [End-to-end grasping policies for human-in-the-loop robots via deep reinforcement learning](https://mlsanity.com/paper/2104.12842/) (2021)

## Built on by (most cited first, 1 of 1)

- [Simulation-Driven Imitation Learning for Biosignals-Free Shared-Autonomy Prosthetic Grasping](https://mlsanity.com/paper/2606.07389/) (2026)

## Abstract (arXiv)

Limb loss affects millions globally, impairing physical function and reducing quality of life. Most traditional surface electromyographic (sEMG) and semi-autonomous methods require users to generate myoelectric signals for each control, imposing physically and mentally taxing demands. This study aims to develop a fully autonomous control system that enables a prosthetic hand to automatically grasp and release objects of various shapes using only a camera attached to the wrist. By placing the hand near an object, the system will automatically execute grasping actions with a proper grip force in response to the hand's movements and the environment. To release the object being grasped, just naturally place the object close to the table and the system will automatically open the hand. Such a system would provide individuals with limb loss with a very easy-to-use prosthetic control interface and may help reduce mental effort while using. To achieve this goal, we developed a teleoperation system to collect human demonstration data for training the prosthetic hand control model using imitation learning, which mimics the prosthetic hand actions from human. By training the model on data from a limited set of objects collected from a single participant's demonstration, we showed that the imitation learning algorithm can achieve high success rates and generalize effectively to new users and previously unseen objects with varying weights. The demonstrations are available at https://sites.google.com/view/autonomous-prosthetic-hand.