Social robotics has emerged as a new research area in recent years. One of the reasons behind this emergence is the rapid pace of improvements in sensor, actuator and processing capabilities in modern hardware enabling robots to interact with humans more effectively than ever before. The motivation for the work presented in this paper is to use advanced human-robot head-eye interaction algorithms in order to create a robotic framework that assists physical therapists treating sensor-motor impairments, such as Autism and Cerebral Palsy by using robotic systems. The robotic platform used in our work is the social robot Zeno, which has a fantastically friendly appearance and bridges the previously reported uncanny valley. In this paper we report on a new coordination algorithm based on reinforcement learning implemented on Zeno for achieving human like head-eye coordination to visually engage patients with cognitive impairments. The experimental results show that the various methods implemented enables social robot Zeno achieve natural head-eye coordination with significant improvement in accuracy without the need of extensive kinematic analysis of the system.

In this paper we discuss control algorithms for real-time interaction between humans and robot manipulators sharing a common workspace. Unlike traditional robotic manipulators, we assume that the interaction between human and robot is not restricted to the wrist/end-effector of the robot, but that it can happen anywhere along the kinematic chain. Interaction forces are measured in some directions, and estimated in others via an Extended Kalman Filter. Sensory measurements used are traditional shaft encoders and also 1 dimensional force sensors via robotic "skin" placed on the robot links. We present simulation results with a CRS A465 showing the performance of our algorithms that compare the impedance response in the presence and absence of force measurements. We also show planned experimental validation on an actual robot using "Quickskin", a piezo-electric skin patch prototype in our lab.

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Recent advances in computing and robot technology create new opportunities for building robots with increasingly more sophisticated interactivity. One such application is the visual interaction between humans and humanoid in tasks such as mimicking and following. Achieving realistic head-eye motion of the humanoid requires understanding of human kinesiology that dictates the way human coordinate head-eye motion and the ability to control the motion of humanoid to move in the same manner that humans do. In this paper we propose an efficient head-eye motion coordination scheme using an optimization approach -an objective function is formed based on human kinesiology and then optimized for obtaining a realistic head-eye trajectory. The tracking robustness during conversational interaction with a human is further enhanced through a visual feedback scheme, which reduces modelling errors of the humanoid hardware. Experimental results show the tracking efficiency and realism of the motion generated by the proposed scheme with Lilly, a humanoid under development in our lab.

Soft tissue images from portable cone beam computed tomography (CBCT) scanners can be used for diagnosis and detection of tumor, cancer, intracerebral hemorrhage, and so forth. Due to large field of view, X-ray scattering which is the main cause of artifacts degrades image quality, such as cupping artifacts, CT number inaccuracy, and low contrast, especially on soft tissue images. In this work, we propose the X-ray scatter correction method for improving soft tissue images. The X-ray scatter correction scheme to estimate X-ray scatter signals is based on the deconvolution technique using the maximum likelihood estimation maximization (MLEM) method. The scatter kernels are obtained by simulating the PMMA sheet on the Monte Carlo simulation (MCS) software. In the experiment, we used the QRM phantom to quantitatively compare with fan-beam CT (FBCT) data in terms of CT number values, contrast to noise ratio, cupping artifacts, and low contrast detectability. Moreover, the PH3 angiography phantom was also used to mimic human soft tissues in the brain. The reconstructed images with our proposed scatter correction show significant improvement on image quality. Thus the proposed scatter correction technique has high potential to detect soft tissues in the brain.