  Many amputees throughout the world are left with limited options to
personally own a prosthetic arm due to the expensive cost, mechanical system
complexity, and lack of availability. The three main control methods of
prosthetic hands are: (1) body-powered control, (2) extrinsic mechanical
control, and (3) myoelectric control. These methods can perform well under a
controlled situation but will often break down in clinical and everyday use due
to poor robustness, weak adaptability, long-term training, and heavy mental
burden during use. This paper lays the complete outline of the design process
of an affordable and easily accessible novel prosthetic arm that reduces the
cost of prosthetics from $10,000 to $700 on average. The 3D printed prosthetic
arm is equipped with a depth camera and closed-loop off-policy deep learning
algorithm to help form grasps to the object in view. Current work in
reinforcement learning masters only individual skills and is heavily focused on
parallel jaw grippers for in-hand manipulation. In order to create
generalization, which better performs real-world manipulation, the focus is
specifically on using the general framework of Markov Decision Process (MDP)
through scalable learning with off-policy algorithms such as deep deterministic
policy gradient (DDPG) and to study this question in the context of grasping a
prosthetic arm. We were able to achieve a 78% grasp success rate on previously
unseen objects and generalize across multiple objects for manipulation tasks.
This work will make prosthetics cheaper, easier to use and accessible globally
for amputees. Future work includes applying similar approaches to other medical
assistive devices where a human is interacting with a machine to complete a
task.
