  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.
