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Cumulatively developing robots offer a unique opportunity to reenact the constant interplay between neural mechanisms related to learning, memory, prospection, and abstraction from the perspective of an integrated system that acts, learns, remembers, reasons, andmakes mistakes. Situated within such interplay lie some of the computationally elusive and fundamental aspects of cognitive behavior: the ability to recall and flexibly exploit diverse experiences of one's past in the context of the present to realize goals, simulate the future, and keep learning further. This article is an adventurous exploration in this direction using a simple engaging scenario of how the humanoid iCub learns to construct the tallest possible stack given an arbitrary set of objects to play with. The learning takes place cumulatively, with the robot interacting with different objects (some previously experienced, some novel) in an openended fashion. Since the solution itself depends on what objects are available in the "now," multiple episodes of past experiences have to be remembered and creatively integrated in the context of the present to be successful. Starting from zero, where the robot knows nothing, we explore the computational basis of organization episodic memory in a cumulatively learning humanoid and address (1) how relevant past experiences can be reconstructed based on the present context, (2) how multiple stored episodic memories compete to survive in the neural space and not be forgotten, (3) how remembered past experiences can be combined with explorative actions to learn something new, and (4) how multiple remembered experiences can be recombined to generate novel behaviors (without exploration). Through the resulting behaviors of the robot as it builds, breaks, learns, and remembers, we emphasize that mechanisms of episodic memory are fundamental design features necessary to enable the survival of autonomous robots in a real world where neither everything can be known nor can everything be experienced. [ABSTRACT FROM AUTHOR]

An accurate measurement of field-grown wheat traits, including spike number, dimension and volume are essential for crop phenotyping and yield analysis. A high-throughput method to image field-grown wheat in three dimensions is presented with an accompanying unsupervised measuring method to obtain individual wheat spike data. Images are captured using four structured light scanners on a field mobile platform, creating dimensionally accurate sub-millimetre resolution 3D point clouds for a 4.5 m3 volume in less than 10 s. The unsupervised method analyses each trial plot's 3D point cloud, containing hundreds of wheat spikes, calculating the average size of the wheat spike and total spike volume per plot. The analysis utilises an adaptive k -means algorithm with dynamic perspectives, to fit each spike's shape and measures the dimensions with a random sample consensus algorithm. The method generates small cuboids to fit all the wheat spikes and estimate the total spikes volume. Experimental results show that the proposed algorithm is a reliable tool for identifying spikes from wheat crops and identifying individual spikes. Compared with the manual measurement, according to the results of five scenes, the average error rate in the number of spikes, spikes' height and spikes' width in tests were 16.27%, 5.24% and 12.38% respectively. • Laser imaging technology for automatic field phenotyping application. • High-throughput field capture platform was constructed. • Unsupervised algorithm allows for wheat dimensions measurement in 3D point clouds. • Automatic measurement can estimate wheat spike size, volume and number of spikes. • Framework was evaluated by comparing the experiment with manual measurement. [ABSTRACT FROM AUTHOR]

Abstract: To exhibit intelligent behavior, cognitive robots must have some knowledge about the consequences of their actions and their value in the context of the goal being realized. We present a neural framework using which explorative sensorimotor experiences of cognitive robots can be efficiently ‘internalized’ using growing sensorimotor maps and planning realized using goal induced quasi-stationary value fields. Further, if there are no predefined reward functions (or the case when they are not good enough in a slightly modified world), the robot will have to try and realize its goal by exploration after which reward/penalty is given at the end. This paper proposes three simple rules for distribution of the received end reward among the contributing neurons in a high dimensional sensorimotor map. Importantly, reward/penalty distribution over hundreds of neurons in the sensorimotor map is computed one shot. This resulting reward distribution can be visualized as an additional value field, representing the new learnt experience and can be combined with other such fields in a context dependent fashion to plan/compose novel emergent behavior. The simplicity and efficiency of the approach is illustrated through the resulting behaviors of the GNOSYS robot in two different scenarios (a) learning ‘when’ to optimize ‘what constraint’ while realizing spatial goals and (b) learning to push a ball intelligently to the corners of a table, while avoiding traps randomly placed by the teacher (this scenario replicates the famous trap tube paradigm from animal reasoning carried out on chimpanzees, capuchins and infants). [Copyright &y& Elsevier]

Unlike a purely reactive system where the motor output is exclusively controlled by the actual sensory input, a cognitive system must be capable of running mental processes which virtually simulate action sequences aimed at achieving a goal. The mental process either attempts to find a feasible course of action compatible with a number of constraints (Internal, Environmental, Task Specific etc) or selects it from a repertoire of previously learned actions, according to the parameters of the task. If neither reasoning process succeeds, a typical backup strategy is to look for a tool that might allow the operator to match all the task constraints. This further necessitates having the capability to alter ones own goal structures to generate sub-goals which must be successfully accomplished in order to achieve the primary goal. In this paper, we introduce a forward/inverse motor control architecture (FMC/IMC) that relaxes an internal model of the overall kinematic chain to a virtual force field applied to the end effector, in the intended direction of movement. This is analogous to the mechanism of coordinating the motion of a wooden marionette by means of attached strings. The relaxation of the FMC/IMC pair provides a general solution for mentally simulating an action of reaching a target position taking into consideration a range of geometric constraints (range of motion in the joint space, internal and external constraints in the workspace) as well as effort-related constraints (range of torque of the actuators, etc.). In case, the forward simulation is successful, the movement is executed; otherwise the residual "error" or measure of inconsistency is taken as a starting point for breaking the action plan into a sequence of sub actions. This process is achieved using a recurrent neural network (RNN) which coordinates the overall reasoning process of framing and issuing goals to the forward inverse models, searching for alternatives tools in solution space and formation of sub-goals based on past context knowledge and present inputs. The RNN + FMC/IMC system is able to successfully reason and coordinate a diverse range of reaching and grasping sequences with/without tools. Using a simple robotic platform (5 DOF Scorbot arm + Stereo vision) we present results of reasoning and coordination of arm/tool movements (real and mental simulation) specifically directed towards solving the classical 2-stick paradigm from animal reasoning at a non linguistic level. [ABSTRACT FROM AUTHOR]

Multiple interlinked factors like demographics, migration patterns, and economics are presently leading to the critical shortage of labour available for low-skilled, physically demanding tasks like soft fruit harvesting. This paper presents a biomimetic robotic solution covering the full 'Perception-Action' loop targeting harvesting of strawberries in a state-of-the-art vertical growing environment. The novelty emerges from both dealing with crop/environment variance as well as configuring the robot action system to deal with a range of runtime task constraints. Unlike the commonly used deep neural networks, the proposed perception system uses conditional Generative Adversarial Networks to identify the ripe fruit using synthetic data. The network can effectively train the synthetic data using the image-to-image translation concept, thereby avoiding the tedious work of collecting and labelling the real dataset. Once the harvest-ready fruit is localised using point cloud data generated by a stereo camera, our platform's action system can coordinate the arm to reach/cut the stem using the Passive Motion Paradigm framework inspired by studies on neural control of movement in the brain. Results from field trials for strawberry detection, reaching/cutting the stem of the fruit, and extension to analysing complex canopy structures/bimanual coordination (searching/picking) are presented. While this article focuses on strawberry harvesting, ongoing research towards adaptation of the architecture to other crops such as tomatoes and sweet peppers is briefly described. [ABSTRACT FROM AUTHOR]

INTRODUCTION Measures to address the COVID-19 pandemic in India included a ban on the sale and use of tobacco products during 2020 when stay at home guidance (lockdown) was in place. In this study we examined the extent of reduction in frequency of tobacco consumption across all products. METHODS Telephone survey was conducted between July and August 2020 across an existing cohort of tobacco users (n=801) residing in Delhi (55.4%) and Chennai (44.6%), India. The participants were recruited irrespective of their gender and use of any kind of tobacco product(s). The survey questionnaire was based on the STOP (Studying Tobacco users Of Pakistan) survey and adapted to the context of smoking and smokeless tobacco use in India. RESULTS Cigarette consumption declined from a median value of 5.0 (IQR: 2-10) sticks in the pre-lockdown period to 2.0 (IQR: 0.4-5) sticks during the lockdown period. Reductions were reported in the daily use of bidis, from 8 (IQR: 4-12) sticks to 5 (IQR: 2-10) sticks and for smokeless tobacco users from 3.5 (IQR: 2-5) packs to 2 (IQR: 1-4) packs during the lockdown. Furthermore, the number of daily cigarette smokers in our cohort decreased from 32.6% (n=261) in the pre-lockdown period to 27.5% (n=220) during lockdown and smokeless tobacco users decreased from 35.8% (n=287) in pre-lockdown period to 30.3% (n=243) during the lockdown period. CONCLUSIONS The decrease in tobacco use can be attributed to various societal and environmental factors. However, the pandemic-linked lockdown provided an opportune condition to reduce the use of tobacco products, which could be due to restricted access and increase in health awareness during the COVID-19 lockdown. [ABSTRACT FROM AUTHOR]

Continued increases in the prevalence of and disproportionate health spending associated with type 2 diabetes argue for policies focused on preventing that condition and treating it appropriately, even as we strive to improve coordination of care for coexisting chronic diseases. This article argues that four policy paradigm shifts will be necessary to achieve that specific emphasis on type 2 diabetes: conceptually integrating primary and secondary prevention along a clinical continuum; recognizing the central importance of early detection of prediabetes and undiagnosed diabetes in implementing cost-effective prevention and control; integrating community and clinical expertise, and resources, within organized and affordable service delivery systems; and sharing and adopting evidence-based policies at the global level. [ABSTRACT FROM AUTHOR]

The present study proposes a computational model for the formation of whole body reaching synergy, i.e., coordinated movements of lower and upper limbs, characterized by a focal component (the hand must reach a target) and a postural component (the center of mass must remain inside the support base). The model is based on an extension of the equilibrium point hypothesis that has been called Passive Motion Paradigm (PMP), modified in order to achieve terminal attractor features and allow the integration of multiple constraints. The model is a network with terminal attractor dynamics. By simulating it in various conditions it was possible to show that it exhibits many of the spatio-temporal features found in experimental data. In particular, the motion of the center of mass appears to be synchronized with the motion of the hand and with proportional amplitude. Moreover, the joint rotation patterns can be accounted for by a single functional degree of freedom, as shown by principal component analysis. It is also suggested that recent findings in motor imagery support the idea that the PMP network may represent the motor cognitive part of synergy formation, uncontaminated by the effect of execution. [ABSTRACT FROM AUTHOR]

This article presents results from a multidisciplinary research project on the integration and transfer of language knowledge into robots as an empirical paradigm for the study of language development in both humans and humanoid robots. Within the framework of human linguistic and cognitive development, we focus on how three central types of learning interact and co-develop: individual learning about one's own embodiment and the environment, social learning (learning from others), and learning of linguistic capability. Our primary concern is how these capabilities can scaffold each other's development in a continuous feedback cycle as their interactions yield increasingly sophisticated competencies in the agent's capacity to interact with others and manipulate its world. Experimental results are summarized in relation to milestones in human linguistic and cognitive development and show that the mutual scaffolding of social learning, individual learning, and linguistic capabilities creates the context, conditions, and requisites for learning in each domain. Challenges and insights identified as a result of this research program are discussed with regard to possible and actual contributions to cognitive science and language ontogeny. In conclusion, directions for future work are suggested that continue to develop this approach toward an integrated framework for understanding these mutually scaffolding processes as a basis for language development in humans and robots. [ABSTRACT FROM AUTHOR]