Upper-limb amputees face tremendous difficulty in operating dexterous powered prostheses. Previous work has shown that aspects of prosthetic hand, wrist, or elbow control can be improved through "intelligent" control, by combining movement-based or gaze-based intent estimation with low-level robotic autonomy. However, no such solutions exist for whole-arm control. Moreover, hardware platforms for advanced prosthetic control are expensive, and existing simulation platforms are not well-designed for integration with robotics software frameworks. We present the Prosthetic Arm Control Testbed (ProACT), a platform for evaluating intelligent control methods for prosthetic arms in an immersive (Augmented Reality) simulation setting. Using ProACT with non-amputee participants, we compare performance in a Box-and-Blocks Task using a virtual myoelectric prosthetic arm, with and without intent estimation. Our results show that methods using intent estimation improve both user satisfaction and the degree of success in the task. To the best of our knowledge, this constitutes the first study of semi-autonomous control for complex whole-arm prostheses, the first study including sequential task modeling in the context of wearable prosthetic arms, and the first testbed of its kind. Towards the goal of supporting future research in intelligent prosthetics, the system is built upon on existing open-source frameworks for robotics.

翻译：上肢截肢者在操作灵巧的动力假肢方面面临巨大困难。先前的研究表明，通过将基于运动或基于注视的意图估计与底层机器人自主性相结合，"智能"控制可以改善假肢手、腕或肘部的控制性能。然而，目前尚不存在针对全臂控制的此类解决方案。此外，用于先进假肢控制的硬件平台成本高昂，而现有的仿真平台未能良好适配机器人软件框架的集成需求。本文提出假肢手臂控制测试平台（ProACT），该平台可在沉浸式（增强现实）仿真环境中评估假肢手臂的智能控制方法。通过让非截肢参与者使用ProACT平台，我们对比了在虚拟肌电假肢手臂上执行积木任务时，有无意图估计条件下的操作表现。实验结果表明，采用意图估计的方法能同时提升用户满意度和任务完成度。据我们所知，这是首次针对复杂全臂假肢的半自主控制研究，首次在可穿戴假肢背景下纳入序列任务建模的研究，亦是首个该类型的测试平台。为支持未来智能假肢研究，本系统基于现有的开源机器人框架构建而成。