Bayesian Nonparametric Feature and Policy Learning for Decision-Making

Learning from demonstrations has gained increasing interest in the recent past, enabling an agent to learn how to make decisions by observing an experienced teacher. While many approaches have been proposed to solve this problem, there is only little work that focuses on reasoning about the observed behavior. We assume that, in many practical problems, an agent makes its decision based on latent features, indicating a certain action. Therefore, we propose a generative model for the states and actions. Inference reveals the number of features, the features, and the policies, allowing us to learn and to analyze the underlying structure of the observed behavior. Further, our approach enables prediction of actions for new states. Simulations are used to assess the performance of the algorithm based upon this model. Moreover, the problem of learning a driver's behavior is investigated, demonstrating the performance of the proposed model in a real-world scenario

Trends in LN-embedding practices at Waikato Institute of Technology (Wintec) in 2019

In this report, we describe the trends in literacy-embedding practices of level-2 and level-3 tutors who worked in vocational contexts at Waikato Institute of Technology (Wintec), and who completed the New Zealand Certificate in Adult Literacy and Numeracy Education (NZCALNE[Voc]) in 2019. We analysed 19 observations, following constructivist grounded theory methodology (Charmaz, 2014), to produce 1302 descriptive labels that highlight literacy and numeracy practices integrated into tutors’ teaching intentionally pursued in a collaborative and mentored training process. Of the initial 12 categories, we conflated the mapping of LN course demands and identifying learners’ LN needs to arrive at a final 11. We then used these categories in an axial analysis (Saldaňa, 2013), categorising the 1302 labels as binaries (i.e. if the label was related to the category, 1 was coded; if not 0 [zero]). The matrix of 14322 ratings of 1s and 0s was then analysed. We calculated the frequency of 1s by category. We argued that the axial analysis allowed us to develop a more holistic perspective which showed how the 1302 labels were configured in relation to the 11 categories of analysis. We concluded that the 11 categories represented key aspects of vocational teaching and training emphasising that LN-embedding practices have to be seamlessly integrated into general pedagogical approaches. A key construct for new tutors is to shape their understanding of seamlessly integrated versus bolted-on LN practices. Our recommendations remain within the whole-of-organisation perspective proposed in the 2017-2018 report (Greyling, 2019)

Hand to mouth: automatic imitation across effector systems

The effector-specificity of automatic imitation was investigated using a stimulus-response compatibility (SRC) procedure in which participants were required to make an open or a close response with either their hand or their mouth. The correct response for each trial was indicated by a pair of letters, and each of these imperative stimuli was accompanied by task-irrelevant action images depicting a hand or mouth opening or closing. Relative to the response, the irrelevant stimulus was either movement compatible or movement incompatible, and either effector compatible or effector incompatible. A movement compatibility effect was observed for both hand and mouth responses. These movement compatibility effects were present when the irrelevant stimulus was effector compatible and when it was effector incompatible, but they were smaller when the irrelevant stimulus and response effectors were incompatible. These findings, which are consistent with the associative sequence learning model of imitation, indicate that automatic imitation is partially effector-specific, and therefore that the effector specificity of intentional and instructed imitation reflects, at least in part, the nature of the mechanisms that mediate visuomotor translation for imitation

Incentives for Boundedly Rational Agents.

This paper develops a theoretical framework for analyzing incentive schemes under bounded rationality. It starts from a standard principal-agent model and then superimposes an assumption of boundedly rational behavior on the part of the agent. Boundedly rational behavior is modeled as an explicit optimization procedure which combines gradient dynamics with a specific form of social learning called imitation of scope.RATIONALITY ; ECONOMIC MODELS ; BEHAVIOUR

Learning at the Ends: From Hand to Tool Affordances in Humanoid Robots

One of the open challenges in designing robots that operate successfully in the unpredictable human environment is how to make them able to predict what actions they can perform on objects, and what their effects will be, i.e., the ability to perceive object affordances. Since modeling all the possible world interactions is unfeasible, learning from experience is required, posing the challenge of collecting a large amount of experiences (i.e., training data). Typically, a manipulative robot operates on external objects by using its own hands (or similar end-effectors), but in some cases the use of tools may be desirable, nevertheless, it is reasonable to assume that while a robot can collect many sensorimotor experiences using its own hands, this cannot happen for all possible human-made tools. Therefore, in this paper we investigate the developmental transition from hand to tool affordances: what sensorimotor skills that a robot has acquired with its bare hands can be employed for tool use? By employing a visual and motor imagination mechanism to represent different hand postures compactly, we propose a probabilistic model to learn hand affordances, and we show how this model can generalize to estimate the affordances of previously unseen tools, ultimately supporting planning, decision-making and tool selection tasks in humanoid robots. We present experimental results with the iCub humanoid robot, and we publicly release the collected sensorimotor data in the form of a hand posture affordances dataset.Comment: dataset available at htts://vislab.isr.tecnico.ulisboa.pt/, IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EpiRob 2017