We present a framework for automatically learning human user models from joint-action demonstrations that enables a robot to compute a robust policy for a collaborative task with a human. First, the demonstrated action sequences are clustered into different human types using an unsupervised learning algorithm. A reward function is then learned for each type through the employment of an inverse reinforcement learning algorithm. The learned model is then incorporated into a mixed-observability Markov decision process (MOMDP) formulation, wherein the human type is a partially observable variable. With this framework, we can infer online the human type of a new user that was not included in the training set, and can compute a policy for the robot that will be aligned to the preference of this user. In a human subject experiment (n = 30), participants agreed more strongly that the robot anticipated their actions when working with a robot incorporating the proposed framework (p < 0.01), compared to manually annotating robot actions. In trials where participants faced difficulty annotating the robot actions to complete the task, the proposed framework significantly improved team efficiency (p < 0.01). The robot incorporating the framework was also found to be more responsive to human actions compared to policies computed using a hand-coded reward function by a domain expert (p < 0.01). These results indicate that learning human user models from joint-action demonstrations and encoding them in a MOMDP formalism can support effective teaming in human-robot collaborative tasks.

While leptin deficiency or dysfunction leads to morbid obesity, obese subjects are characterized paradoxically by hyperleptinemia indicating lack of response to leptin. C-reactive protein (CRP) has been suggested to be a key plasma protein that could bind to leptin. To examine whether CRP interferes with leptin action, mediated through its cell surface receptor, docking studies of CRP with the extracellular domain of the leptin receptor were done employing bioinformatics tools. Monomeric CRP docked with better Z-rank score and more non-bond interactions than pentameric CRP at the CRH2–FNIII domain proximal to the cell membrane, distinct from the leptin-docking site. Interaction of CRP with leptin receptor was validated by solid phase binding assay and co-immunoprecipitation of CRP and soluble leptin receptor (sOb R) from human plasma. Analysis of the serum levels of leptin, CRP, and sOb R by ELISA showed that CRP levels were significantly elevated (p < 0.0001) in non-morbid obese subjects (n = 42) compared to lean subjects (n = 32) and correlated positively with body mass index (BMI) (r = 0.74, p < 0.0001) and leptin (r = 0.8, p < 0.0001); levels of sOb R were significantly low in obese subjects (p < 0.001) and showed a negative correlation with BMI (r = −0.26, p < 0.05) and leptin (r = −0.23, p < 0.05) indicating a minimal role for sOb R in sequestering leptin.

We design and evaluate a method of human-robot cross-training, a validated and widely used strategy for the effective training of human teams. Cross-training is an interactive planning method in which team members iteratively switch roles with one another to learn a shared plan for the performance of a collaborative task. We first present a computational formulation of the robot mental model, which encodes the sequence of robot actions necessary for task completion and the expectations of the robot for preferred human actions, and show that the robot model is quantitatively comparable to the mental model that captures the inter-role knowledge held by the human. Additionally, we propose a quantitative measure of robot mental model convergence and an objective metric of model similarity. Based on this encoding, we formulate a human-robot cross-training method and evaluate its efficacy through experiments involving human subjects (n = 60). We compare human-robot cross-training to standard reinforcement learning techniques, and show that cross-training yields statistically significant improvements in quantitative team performance measures, as well as significant differences in perceived robot performance and human trust. Finally, we discuss the objective measure of robot mental model convergence as a method to dynamically assess human errors. This study supports the hypothesis that the effective and fluent teaming of a human and a robot may best be achieved by modeling known, effective human teamwork practices.

Malignant proliferating trichilemmal tumour is a rare cutaneous malignant neoplasm usually occurring in elderly women. We present a case of malignant trichilemmal tumour in a young lady of 26 years of age with a previous history of proliferating trichilemmal tumour at the same site.