Abstract
Grasp using a prosthetic hand in real life can be a difficult task. The amputee users are often capable of planning the reaching trajectory and hand grasp location selection, however, failed in precise finger movements, such as adapting the fingers to the surface of the object without excessive force. It is much efficient to leave that part to the machine autonomy. In order to combine the intention and planning ability of users with robotic control, the shared control is introduced in which users’ inputs and robot control methods are combined to achieve a goal. The shared control problem can be formulated as a Partially Observable Markov Decision Process. To find the optimal control policy, we adopt an adaptive dynamic programming and reinforcement learning-based control algorithm-Deep Deterministic Policy Gradient combined with Hindsight Experience Replay. We proposed the algorithm with a prediction layer using the reparameterization technique. The system was tested in a modified simulation environment for the ability to follow the user’s intention and keep the contact force in boundary for safety.
Highlights
A major task for an anthropomorphic prosthetic hand is to perform a dexterous and stable grasping in daily life.[1,2] A success grasp control consists of problems in different phases
Prosthetic hand control for amputees usually takes a long time in training and is hard for the user to comprehend the states of the robotic hand
A result is shown in Figure 5, in which the dashed lines are the goals of corresponding fingertips and the solid lines are the positions of the fingertips
Summary
A major task for an anthropomorphic prosthetic hand is to perform a dexterous and stable grasping in daily life.[1,2] A success grasp control consists of problems in different phases. In the pre-grasp phase, the control problem is about grasp planning which can be addressed as several complex factors including positioning the arm, orienting the wrist, and shaping the fingers subject to object placement and distribution, environment obstacles, and so on. On most occasions, these factors lie in a higher multimodal dimension and the solution may be intractable. SC makes fine adjustments to the fingers by processing information from the force sensors placed on the prosthetic hand fingers.[9] In the pre-contact grasp phase, the movement planning can be done by users to achieve better embodiment since it is more intuitive. SC strategies aim to bridge the gap between human intentions and efficient execution of the intended task by using information from the sensors
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