When a human performs a task with the assistance of wearable extra limbs, the human movement for performing the task may inadvertently disturb the position and orientation of the robot base, making it difficult for the robot to properly carry out its objective. Therefore, unlike self-standing robots, a wearable robot must not only assist the user without interfering or prohibiting the natural human movement, but also have the capability to detect and reject disturbances caused by the wearer's motion. This paper examines such a situation, where the human attempts to twist open a bottle while a pair of robotic fingers mounted on the same arm holds the bottle in place. As the human arm rotates to twist the cap, the robot and consequently the bottle would rotate in that same direction, which makes separation of the cap from the bottle almost impossible. To compensate for the human induced disturbances, a data-driven latent space impedance control method is developed such that the robot can secure the bottle and at the same time allow natural human movement to be carried out during manipulation. Simulation and experiments have demonstrated the efficacy of the latent space impedance controller to enable single-handed object manipulation with the assistance of wearable robotic fingers.

Functionality of a human hand can be augmented with wearable robotic fingers to enable grasping and manipulation of objects with a single hand. Such technology will have applications in manufacturing and construction, as well as health care. This paper presents a method for controlling extra robotic fingers, termed “Supernumerary Robotic Fingers (SR Fingers),” in coordination with human fingers to grasp diverse objects. Two hypotheses are proposed and verified through experiments. One is that humans prefer grasp posture of their fingers and that of the SR Fingers to be highly correlated when working together, which is represented with a few principal components, resembling grasp synergy in neuromotor control. The other hypothesis is that SR Finger posture can be controlled to coordinate with human finger posture via grasp synergy of the hybrid human-robotic hand. Partial least squares regression is used for predicting a desired posture of the SR Fingers from the measurement of human fingers. This method is implemented on a pair of wrist-mounted SR Fingers. Experiments demonstrate that the prototype SR Fingers can assist the human user in performing single-handed grasping tasks without requiring explicit commands.

Functionality of a human hand can be augmented with extra robotic fingers attached to the forearm. These wearable extra fingers can work together with the human fingers and perform tasks that are usually difficult for a single hand. Such technology may benefit amputees and surviving stroke patients who are forced to live with only one functional arm. In this paper, we present a novel method, exploiting redundancies in the elbow, to control a pair of extra robotic fingers, termed Supernumerary Robotic Fingers or SR Fingers for short, to perform “hold-and-manipulate” tasks. Two control protocols are explored here. One protocol enables discrete transition between different modes of motion, while the other continuously controls grasping behavior of the SR Fingers. Four robot assisted “hold-and-manipulate” tasks are examined, including twisting off the cap of a water bottle, opening the lid of a food container, plugging a cable into an electronic device, and mixing salad in a bowl. Through the successful completion of these tasks, we demonstrate that wearable extra fingers have the potential to provide those with impaired hands the opportunity to live with more independence and work more productively.

Most of magnetic localization and orientation systems use single dipole models to calculate magnetic field, which, due to the fundamental limitation of the dipole, become inaccurate as the sensors approach the surface of the magnet. Moreover, they are unable to account for geometry, magnetization and any physical imperfections of the magnetic source. This paper presents a novel method of modeling the magnetic field of axisymmetric permanent magnets with artificial neural networks (ANNs), which permits accurate field modeling even at close proximity to the magnet. ANN based field models used to characterize experimental field data of solid and annular cylindrical magnets were found to be on average at least 10 times more accurate than that of dipole based models. Using model-based localization, tracking results from following a predetermined figure `8' path were also promising, with an average error of 0.43 mm in XY plane and 0.93 mm in XZ plane from only three sensor inputs.