# Simulation-Driven Imitation Learning for Biosignals-Free Shared-Autonomy Prosthetic Grasping

arXiv: https://arxiv.org/abs/2606.07389 · HTML: https://mlsanity.com/paper/2606.07389/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 29 of its 64 references that are in the map.
- First posted on arXiv 5 Jun 2026.

## Placed in

- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

## Builds on (most cited first, 29 of 29)

- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Items](https://mlsanity.com/paper/2204.11918/) (2022)
- [Behavioral Cloning from Observation](https://mlsanity.com/paper/1805.01954/) (2018)
- [DexYCB: A Benchmark for Capturing Hand Grasping of Objects](https://mlsanity.com/paper/2104.04631/) (2021)
- [HG-DAgger: Interactive Imitation Learning with Human Experts](https://mlsanity.com/paper/1810.02890/) (2018)
- [Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes](https://mlsanity.com/paper/2103.14127/) (2021)
- [Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks](https://mlsanity.com/paper/1812.07252/) (2018)
- [A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges](https://mlsanity.com/paper/2309.02473/) (2023)
- [DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation](https://mlsanity.com/paper/2210.02697/) (2022)
- [Infinigen Indoors: Photorealistic Indoor Scenes using Procedural Generation](https://mlsanity.com/paper/2406.11824/) (2024)
- [Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias](https://mlsanity.com/paper/1807.07049/) (2018)
- [Residual Off-Policy RL for Finetuning Behavior Cloning Policies](https://mlsanity.com/paper/2509.19301/) (2025)
- [Vision-Based Manipulators Need to Also See from Their Hands](https://mlsanity.com/paper/2203.12677/) (2022)
- [DexGraspNet 2.0: Learning Generative Dexterous Grasping in Large-scale Synthetic Cluttered Scenes](https://mlsanity.com/paper/2410.23004/) (2024)
- [Domain Randomization and Generative Models for Robotic Grasping](https://mlsanity.com/paper/1710.06425/) (2017)
- [Data-driven Planning via Imitation Learning](https://mlsanity.com/paper/1711.06391/) (2017)
- [Human-in-the-Loop Task and Motion Planning for Imitation Learning](https://mlsanity.com/paper/2310.16014/) (2023)
- [GraspGF: Learning Score-based Grasping Primitive for Human-assisting Dexterous Grasping](https://mlsanity.com/paper/2309.06038/) (2023)
- [BODex: Scalable and Efficient Robotic Dexterous Grasp Synthesis Using Bilevel Optimization](https://mlsanity.com/paper/2412.16490/) (2024)
- [MultiGripperGrasp: A Dataset for Robotic Grasping from Parallel Jaw Grippers to Dexterous Hands](https://mlsanity.com/paper/2403.09841/) (2024)
- [Domain Randomization for Sim2real Transfer of Automatically Generated Grasping Datasets](https://mlsanity.com/paper/2310.04517/) (2023)
- [Proprioceptive Sonomyographic Control: A novel method of intuitive proportional control of multiple degrees of freedom for upper-extremity amputees](https://mlsanity.com/paper/1808.06543/) (2018)
- [A Powered Prosthetic Hand with Vision System for Enhancing the Anthropopathic Grasp](https://mlsanity.com/paper/2412.07105/) (2024)
- [Bring Your Own Grasp Generator: Leveraging Robot Grasp Generation for Prosthetic Grasping](https://mlsanity.com/paper/2503.00466/) (2025)
- [Towards Biosignals-Free Autonomous Prosthetic Hand Control via Imitation Learning](https://mlsanity.com/paper/2506.08795/) (2025)
- [HannesImitation: Grasping with the Hannes Prosthetic Hand via Imitation Learning](https://mlsanity.com/paper/2508.00491/) (2025)

## Abstract (arXiv)

Biosignals-free shared-autonomy control of upper-limb prosthetic hands aims to enable natural and low-effort manipulation without relying on EMG or other physiological signals. Recent imitation-learning-based approaches have shown promising results, but their scalability is limited by the cost and variability of collecting large amounts of real-world human demonstration data. In this work, we present a scalable simulation framework that automatically generates diverse reach-to-grasp demonstrations from a wrist-mounted virtual camera. The framework combines physically feasible grasp synthesis, natural reaching trajectories retargeting, and reach--grasp--lift execution in procedurally generated indoor environments. It records wrist-view observations, proprioception, and actions to build a large-scale demonstration dataset for imitation learning. Through extensive simulation benchmarks, we evaluate object and scene generalization and compare several representative state-of-the-art imitation learning methods. Results show that the simulated demonstrations are sufficiently rich and consistent for effective policy learning. In three realistic settings, the learned sim-to-real policy achieves over 90% grasp success, surpasses baseline methods, and exhibits stronger generalization, highlighting the promise of simulation-driven training for biosignals-free shared-autonomy prosthetic grasping. The demonstrations are available at hrefhttps://sites.google.com/view/sim-prosthetic-grasp/homehttps://sites.google.com/view/sim-prosthetic-grasp/home.