We present the Robot Gesture Library (RoGuE), amotion-planning approach to generating gestures. Gestures improve robot communication skills, strengthening robots as partners in a collaborative setting. Previouswork maps from environment scenario to gesture selection. This work maps from gesture selection to gesture execution. We create a flexible and common language by parameterizing gestures as task-space constraints onrobot trajectories and goals. This allows us to leverage powerful motion planners and to generalize across environments and robot morphologies. We demonstrateRoGuE on four robots: HREB, ADA, CURI and the PR2.

Extended school absence during K-12 education can have anegative impact on both the educational and social development of a child. Mobile Remote Presence (MRP) can helpenable continued access to K-12 education for children withhealth challenges. However, most MRP platforms are targetedtowards adult users in domains such as the workplace.The importance of social interaction and engagement in K-12 education creates a unique set of needs and challenges foran MRP platform. In this work, we discuss the benefits ofMRP usage for K-12 education, ongoing challenges for MRPacross domains, and the requirements of an MRP platform forthe classroom.

In this position paper we introduce our approach to positive computing by developing and integrating methods for future assistant and companion agents which could help us a) avoid making mistakes due to biases caused by insufficient knowledge, b) be more empathic and righteous, c) be more sensitive and thoughtful. We present text processing techniques for automatic discovery of possible reasoning errors and provide hints to make users doubt their beliefs when there is a possibility of harm. We present existing sources and methods, discuss on how natural language processing technologies could contribute to various aspects of well-being by giving examples of systems we develop, and describe the strengths and weaknesses of our approach.

Artificially intelligent conversational agents have been demonstrated to positively impact team based learning in classrooms and hold even greater potential for impact in the now widespread Massive Open Online Courses (MOOCs) if certain challenges can be overcome. These challenges include team formation, coordination and management of group processes in teams working together while distributed both in time and space. Our work begins with an architecture for orchestrating conversational agent based support for group learning called Bazaar, which has facilitated numerous successful studies of learning in the past including some early investigations in MOOC contexts. In this paper, we briefly describe our experience in designing, developing and deploying agent supported collaborative learning activities in 3 different MOOCs in three iterations. Findings from this iterative design process provide an empirical foundation for a reusable framework for facilitating similar activities in future MOOCs.

People increasingly rely on machine learning (ML) to make intelligent decisions. However, the ML results are often difficult to interpret and the algorithms do not support interaction to solicit clarification or explanation. In this paper, we highlight an emerging research area of interpretable explanations for transfer learning in sequential tasks, in which an agent must explain how it learns a new task given prior, common knowledge. The goal is to enhance a user's ability to trust and use the system output and to enable iterative feedback for improving the system. We review prior work in probabilistic systems, sequential decision-making, interpretable explanations, transfer learning, and interactive machine learning, and identify an intersection that deserves further research focus. We believe that developing adaptive, transparent learning models will build the foundation for better human-machine systems in applications for elder care, education, and health care.

Researchers have been developing Social robots are increasingly common in schools to support algorithms to aid robots in determining task hierarchies learning goals, in workplaces to augment productivity, (Hayes and Scassellati 2014), learning tasks from humans and in homes to improve quality of life. The fulfillment of (Thomaz and Breazeal 2008), and choosing what information their objectives in these environments are strongly dependent to communicate and when to communicate it (Unhelkar on the quality of the sustained, supportive relationship and Shah 2016). Although robots have made great robots are able to construct with their human users.

Methods and materials are described for employing a human-shaped robot as a lecturer in automated remote instruction. Video segments from the stimuli of a 2 (participant substrate: VR or non-VR) x 2 (robot embodiment: copresent or screen) balanced between-participants experiment are provided. In each condition, a robot delivers the content for a lecture on the nutrition of carbohydrates. The robot uses identical speech and body movement while the same set of slides plays on an adjacent computer, thereby controlling for such factors as educational content, robot appearance and robot size. The experiment employs Aldebaran’s 25-degrees-of-freedom Nao as the robot and the Oculus Rift as the immersive VR system. The lecture speech and slides were obtained with permission from a Mandarin Chinese-language online course and translated into English. The setup for different delivery modes for automated remote instruction are illustrated using a robot delivering foreign language online content. These methods support the design and evaluation of robots that perform the role of lecturer.

Despite the advances made in artificial intelligence, software agents, and robotics, there is little we see today that we can truly call a fully autonomous system. We conjecture that the main inhibitor for advancing autonomy is lack of trust. Trusted autonomy is the scientific and engineering field to establish the foundations and ground work for developing trusted autonomous systems (robotics and software agents) that can be used in our daily life, and can be integrated with humans seamlessly, naturally and efficiently. In this paper, we review this literature to reveal opportunities for researchers and practitioners to work on topics that can create a leap forward in advancing the field of trusted autonomy. We focus the paper on the `trust' component as the uniting technology between humans and machines. Our inquiry into this topic revolves around three sub-topics: (1) reviewing and positioning the trust modelling literature for the purpose of trusted autonomy; (2) reviewing a critical subset of sensor technologies that allow a machine to sense human states; and (3) distilling some critical questions for advancing the field of trusted autonomy. The inquiry is augmented with conceptual models that we propose along the way by recompiling and reshaping the literature into forms that enables trusted autonomous systems to become a reality. The paper offers a vision for a Trusted Cyborg Swarm, an extension of our previous Cognitive Cyber Symbiosis concept, whereby humans and machines meld together in a harmonious, seamless, and coordinated manner.

Stochastic Gradient Descent (SGD) is one of the most widely used techniques for online optimization in machine learning. In this work, we accelerate SGD by adaptively learning how to sample the most useful training examples at each time step. First, we show that SGD can be used to learn the best possible sampling distribution of an importance sampling estimator. Second, we show that the sampling distribution of an SGD algorithm can be estimated online by incrementally minimizing the variance of the gradient. The resulting algorithm -- called Adaptive Weighted SGD (AW-SGD) -- maintains a set of parameters to optimize, as well as a set of parameters to sample learning examples. We show that AW-SGD yields faster convergence in three different applications: (i) image classification with deep features, where the sampling of images depends on their labels, (ii) matrix factorization, where rows and columns are not sampled uniformly, and (iii) reinforcement learning, where the optimized and exploration policies are estimated at the same time, where our approach corresponds to an off-policy gradient algorithm.

Human infants can discover words directly from unsegmented speech signals without any explicitly labeled data. In this paper, we develop a novel machine learning method called nonparametric Bayesian double articulation analyzer (NPB-DAA) that can directly acquire language and acoustic models from observed continuous speech signals. For this purpose, we propose an integrative generative model that combines a language model and an acoustic model into a single generative model called the "hierarchical Dirichlet process hidden language model" (HDP-HLM). The HDP-HLM is obtained by extending the hierarchical Dirichlet process hidden semi-Markov model (HDP-HSMM) proposed by Johnson et al. An inference procedure for the HDP-HLM is derived using the blocked Gibbs sampler originally proposed for the HDP-HSMM. This procedure enables the simultaneous and direct inference of language and acoustic models from continuous speech signals. Based on the HDP-HLM and its inference procedure, we developed a novel double articulation analyzer. By assuming HDP-HLM as a generative model of observed time series data, and by inferring latent variables of the model, the method can analyze latent double articulation structure, i.e., hierarchically organized latent words and phonemes, of the data in an unsupervised manner. The novel unsupervised double articulation analyzer is called NPB-DAA. The NPB-DAA can automatically estimate double articulation structure embedded in speech signals. We also carried out two evaluation experiments using synthetic data and actual human continuous speech signals representing Japanese vowel sequences. In the word acquisition and phoneme categorization tasks, the NPB-DAA outperformed a conventional double articulation analyzer (DAA) and baseline automatic speech recognition system whose acoustic model was trained in a supervised manner.