The autonomous learning of new goals in robotics remains a complex issue to address. Here, we propose a model where curiosity influence learning flexibility. To do so, this paper proposes to root curiosity and attention together by taking inspiration from the Locus Coeruleus-Norepinephrine system along with various cognitive processes such as cognitive persistence and visual habituation. We apply our approach by experimenting with a simulated robotic arm on a set of objects with varying difficulty. The robot first discovers new goals via bottom-up attention through motor babbling with an inhibition of return mechanism, then engage to the learning of goals due to neural activity arising within the curiosity mechanism. The architecture is modelled with dynamic neural fields and the learning of goals such as pushing the objects in diverse directions is supported by the use of forward and inverse models implemented by multi-layer perceptrons. The adoption of dynamic neural fields to model curiosity, habituation and persistence allows the robot to demonstrate various learning trajectories depending on the object. In addition, the approach exhibits interesting properties regarding the learning of similar goals as well as the continuous switch between exploration and exploitation.

The escalating prevalence of cannabis use, and associated cannabis-use disorder (CUD), poses a significant public health challenge globally. With a notably wide treatment gap, especially among emerging adults (EAs; ages 18-25), addressing cannabis use and CUD remains a pivotal objective within the 2030 United Nations Agenda for Sustainable Development Goals (SDG). In this work, we develop an online reinforcement learning (RL) algorithm called reBandit which will be utilized in a mobile health study to deliver personalized mobile health interventions aimed at reducing cannabis use among EAs. reBandit utilizes random effects and informative Bayesian priors to learn quickly and efficiently in noisy mobile health environments. Moreover, reBandit employs Empirical Bayes and optimization techniques to autonomously update its hyper-parameters online. To evaluate the performance of our algorithm, we construct a simulation testbed using data from a prior study, and compare against commonly used algorithms in mobile health studies. We show that reBandit performs equally well or better than all the baseline algorithms, and the performance gap widens as population heterogeneity increases in the simulation environment, proving its adeptness to adapt to diverse population of study participants.

Effective and fluent close-proximity human-robot interaction requires understanding how humans get habituated to robots and how robot motion affects human comfort. While prior work has identified humans' preferences over robot motion characteristics and studied their influence on comfort, we are yet to understand how novice first-time robot users get habituated to robots and how robot motion impacts the dynamics of comfort over repeated interactions. To take the first step towards such understanding, we carry out a user study to investigate the connections between robot motion and user comfort and habituation. Specifically, we study the influence of workspace overlap, end-effector speed, and robot motion legibility on overall comfort and its evolution over repeated interactions. Our analyses reveal that workspace overlap, in contrast to speed and legibility, has a significant impact on users' perceived comfort and habituation. In particular, lower workspace overlap leads to users reporting significantly higher overall comfort, lower variations in comfort, and fewer fluctuations in comfort levels during habituation.

Artificial Intelligence has surely stormed mankind in past years, the machines are extremely good at imitating what we tell them to do. But AI and creativity are counterparts, creativity is the abstract concept that is still missing from the core AI field. For the past few years, researchers have been trying to decode the machine's ability to mimic human-level intelligence to generate creative products such as jokes, poetry, problems, paintings, music, etc. The integral aim is to show that AI algorithms are in-fact intelligent enough to produce art without involving human artists but taking into account the human creative products in the learning process. Several interesting algorithms like GANs(Generative Adversarial Networks) have been introduced to explore the creative space.

Slime molds are among the world's strangest organisms. Long mistaken for fungi, they are now classed as a type of amoeba. As single-celled organisms, they have neither neurons nor brains. Yet for about a decade, scientists have debated whether slime molds have the capacity to learn about their environments and adjust their behavior accordingly. Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.

It's easy to feel like new ML technologies for us to rethink everything about UX design, but that's not quite true. The emergence of ML doesn't change the fact that the most usable, delightful UIs are those that embody principles of good design--like habituation--that many designers and researchers (Don Norman, Jakob Nielsen, Steve Krug, and Jeff Johnson to name a few) have been writing about for years. Evaluating recommendations or visually searching the interface for content counts as a navigation step, just like a tap or click. No ML-based suggestion will be "helpful" enough to offset breaking your user's flow state and muscle memory. But if you're confident that the user has a more open-ended goal like exploration, you have more leeway to put dynamic, ML-based features at the forefront of your UI.

You don't need a brain to learn. Slime molds, for example, solve mazes and navigate obstacles--all without a single neuron. Information about their environment is somehow stored across their bodies. A new paper suggests that samarium nickelate oxide (SNO, for short), a synthetic crystal, can mimic learning. SNO's ability comes from its environmental sensitivity.

Known in behaviour as "habituation" and in perception as "adaptation", it has been recognised from time immemorial yet still lacks explanation. Only recently Sharpless and Jasper (1956, ref. 10) could say "Habituaticn... has yet to be explained by any known neurophysiological principles". A review of the subject need not be given here as it has been well reviewed by Humphrey (1933, ref.6), Harris (1943, ref. 5), and Thorpe (1956, ref. 11). On one important matter they are agreed: habituation of typical form occurs in almost every form of life; in particular it appears as readily in forms having no neural apparatus as in the forms having a well developed brain. Amoeba shows it as freely as does the cat. The phenomenon evidently does not depend on specifically neurophysiological details. Its origin must lie in some property of much wider occurrence. The possibility of "fatigue" as an explanation must be rejected.

Before the age of 4 months, infants make inductive inferences about the motions of physical objects. Developmental psychologists have provided verbal accounts of the knowledge that supports these inferences, but often these accounts focus on categorical rather than probabilistic principles. We propose that infant object perception is guided in part by probabilistic principles like persistence: things tend to remain the same, and when they change they do so gradually. To illustrate this idea, we develop an ideal observer model that includes probabilistic formulations of rigidity and inertia. Like previous researchers, we suggest that rigid motions are expected from an early age, but we challenge the previous claim that expectations consistent with inertia are relatively slow to develop (Spelke et al., 1992). We support these arguments by modeling four experiments from the developmental literature.

His present interests are: study of complex equilibria, especially in their topological aspects, as applied to the intelligent and adaptive aspects of the brain. He is now in the Department of Research of Barnwood House Hospital, Gloucester.