In robot-assisted upper limb rehabilitation, detecting the intentions of hemiplegic patients is essential towards assisting the patients to actively exercise instead of driving passive motions. Many interactive channels, such as voice, EMG and EEG, have been studied to estimate the motion intentions. However, limitations of these techniques, such as high complexity, have constrained their applications in practice. In this paper, we integrate a virtual environment and a low-cost motion sensor into a novel control strategy to detect motion intentions for a rehabilitation robot. Several bimanual motion sequences are intuitively programmed by a professional therapist for subjects to repeat. The strategy uses the unaffected arm and the programmed motion sequence to estimate the motion intentions of the affected arm. We adopt this strategy in Mirror Therapy, a widely-practised therapeutic intervention method. Experiments have been conducted to validate the control strategy.

The kinematic modelling has been applied to many controllers of under-actuated manipulators. Most of these studies assume that the control process is conducted within the workspace. However, as such a kinematic model cannot describe the situations when the stable grasping is violated in the real environment, these controllers may fail unexpectedly. In this paper, we propose a combination of kinematics based Workspace Analysis (WA) and Gaussian Process Classification (GPC) to model the success rates of control actions in the theoretical workspace. We also use the Gaussian Process Regression (GPR) to model the residual between the prediction of the WA and the ground truth data. We then apply this integrated model, Gaussian Processes enhanced Workspace Analysis (GP-WA), into an optimal controller. The optimal controller is implemented on a planar under-actuated gripper with two three-phalanx fingers. Two sets of simulation experiments are carried out to validate our method. The results demonstrate that the optimal manipulation controller based on GP-WA achieves high control accuracy for manipulating a wide range of objects.

Many recent studies describe micromanipulation systems by using complex Analytic Forward Models (AFM), but such models are difficult to build and incapable of describing unmodelable factors, such as manufacturing defects. In this work, we propose the Enhanced Analytic Forward Model (EAFM), an integrated model of the AFM and the Heteroscedastic Gaussian Processes (HGP). The EAFM can compensate the shortfalls of the AFM by training the HGP on the residual of the AFM. This also allows the HGP to learn the repeatability of the micromanipulation system. Based on the EAFM, we further contribute an optimal position controller for improving the accuracy and the repeatability. This optimal EAFM controller is implemented and tested on a three degree-of-freedom micromanipulator based micromanipulation system. Two sets of real-world experiments are carried out to verify our method. The results demonstrate that the controller using EAFM can statistically achieve higher accuracy and repeatability than solely using the AFM.

Recent studies on underactuated manipulation usually describe the system with a Kinematic Model (KM), which is built by adding external constraints to the standard manipulation analysis method. However, such external constraints are easily violated in a real-world dexterous manipulation task which results in significant control errors. In this work, the Enhanced Kinematic Model (E-KM), an integrated model of the KM and the Sparse Online Gaussian Process (SOGP) is proposed. The E-KM can compensate the shortfalls of the KM by on-the-fly training the SOGP on the residual between the prediction of the KM and the ground truth data. Based on the E-KM, we further contribute an optimal controller for underactuated manipulations. This optimal E-KM controller is implemented and tested on the iCub, a humanoid robot with two anthropomorphic underactuated hands. Two sets of real-world experiments are carried out to verify our method. The results demonstrate that the controller using E-KM statistically can achieve higher control accuracy than using solely using the KM for a wide range of objects.

In this work, we are primarily concerned with robotic systems that learn online and continuously from multi-variate data-streams. Our first contribution is a new recursive kernel, which we have integrated into a sparse Gaussian Process to yield the Spatio-Temporal Online Recursive Kernel Gaussian Process (STORK-GP). This algorithm iteratively learns from time-series, providing both predictions and uncertainty estimates. Experiments on benchmarks demonstrate that our method achieves high accuracies relative to state-of-the-art methods. Second, we contribute an online tactile classifier which uses an array of STORK-GP experts. In contrast to existing work, our classifier is capable of learning new objects as they are presented, improving itself over time. We show that our approach yields results comparable to highly-optimised offline classification methods. Moreover, we conducted experiments with human subjects in a similar online setting with true-label feedback and present the insights gained.