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 \href{https://sites.google.com/view/sim-prosthetic-grasp/home}{https://sites.google.com/view/sim-prosthetic-grasp/home}.

翻译：免生物信号共享自主控制的上肢假肢手旨在无需依赖肌电信号或其他生理信号实现自然且低力的操控。近期基于模仿学习的方法展现出良好前景，但其可扩展性受限于大规模真实人类演示数据采集的成本和变异性。本研究提出一种可扩展的仿真框架，可从腕部虚拟相机自动生成多样化的抓取演示。该框架结合了物理可行的抓取综合、自然抓取轨迹重定向，以及在程序化生成的室内环境中执行"抓取-提起"操作。通过记录腕部视角观测、本体感知和动作数据，构建用于模仿学习的大规模演示数据集。基于大量仿真基准测试，我们评估了对象与场景的泛化能力，并对比了多种代表性先进模仿学习方法。结果表明，仿真演示数据具有充分的丰富性和一致性，可支撑有效的策略学习。在三种逼真场景中，所学仿真到现实策略的抓取成功率超过90%，超越基线方法并展现出更强的泛化能力，凸显了基于仿真驱动的训练在免生物信号共享自主假肢抓取中的潜力。演示数据集可通过\href{https://sites.google.com/view/sim-prosthetic-grasp/home}{https://sites.google.com/view/sim-prosthetic-grasp/home}获取。