Unscented Bayesian Optimization for Safe Robot Grasping

We address the robot grasp optimization problem of unknown objects considering uncertainty in the input space. Grasping unknown objects can be achieved by using a trial and error exploration strategy. Bayesian optimization is a sample efficient optimization algorithm that is especially suitable for this setups as it actively reduces the number of trials for learning about the function to optimize. In fact, this active object exploration is the same strategy that infants do to learn optimal grasps. One problem that arises while learning grasping policies is that some configurations of grasp parameters may be very sensitive to error in the relative pose between the object and robot end-effector. We call these configurations unsafe because small errors during grasp execution may turn good grasps into bad grasps. Therefore, to reduce the risk of grasp failure, grasps should be planned in safe areas. We propose a new algorithm, Unscented Bayesian optimization that is able to perform sample efficient optimization while taking into consideration input noise to find safe optima. The contribution of Unscented Bayesian optimization is twofold as if provides a new decision process that drives exploration to safe regions and a new selection procedure that chooses the optimal in terms of its safety without extra analysis or computational cost. Both contributions are rooted on the strong theory behind the unscented transformation, a popular nonlinear approximation method. We show its advantages with respect to the classical Bayesian optimization both in synthetic problems and in realistic robot grasp simulations. The results highlights that our method achieves optimal and robust grasping policies after few trials while the selected grasps remain in safe regions.Comment: conference pape

Bioengineered Textiles and Nonwovens – the convergence of bio-miniaturisation and electroactive conductive polymers for assistive healthcare, portable power and design-led wearable technology

Today, there is an opportunity to bring together creative design activities to exploit the responsive and adaptive ‘smart’ materials that are a result of rapid development in electro, photo active polymers or OFEDs (organic thin film electronic devices), bio-responsive hydrogels, integrated into MEMS/NEMS devices and systems respectively. Some of these integrated systems are summarised in this paper, highlighting their use to create enhanced functionality in textiles, fabrics and non-woven large area thin films. By understanding the characteristics and properties of OFEDs and bio polymers and how they can be transformed into implementable physical forms, innovative products and services can be developed, with wide implications. The paper outlines some of these opportunities and applications, in particular, an ambient living platform, dealing with human centred needs, of people at work, people at home and people at play. The innovative design affords the accelerated development of intelligent materials (interactive, responsive and adaptive) for a new product & service design landscape, encompassing assistive healthcare (smart bandages and digital theranostics), ambient living, renewable energy (organic PV and solar textiles), interactive consumer products, interactive personal & beauty care (e-Scent) and a more intelligent built environment

On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation

Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas

Search algorithms as a framework for the optimization of drug combinations

Combination therapies are often needed for effective clinical outcomes in the management of complex diseases, but presently they are generally based on empirical clinical experience. Here we suggest a novel application of search algorithms, originally developed for digital communication, modified to optimize combinations of therapeutic interventions. In biological experiments measuring the restoration of the decline with age in heart function and exercise capacity in Drosophila melanogaster, we found that search algorithms correctly identified optimal combinations of four drugs with only one third of the tests performed in a fully factorial search. In experiments identifying combinations of three doses of up to six drugs for selective killing of human cancer cells, search algorithms resulted in a highly significant enrichment of selective combinations compared with random searches. In simulations using a network model of cell death, we found that the search algorithms identified the optimal combinations of 6-9 interventions in 80-90% of tests, compared with 15-30% for an equivalent random search. These findings suggest that modified search algorithms from information theory have the potential to enhance the discovery of novel therapeutic drug combinations. This report also helps to frame a biomedical problem that will benefit from an interdisciplinary effort and suggests a general strategy for its solution.Comment: 36 pages, 10 figures, revised versio

Synergetic and redundant information flow detected by unnormalized Granger causality: application to resting state fMRI

Objectives: We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network. Methods: The presence of redundancy and/or synergy in multivariate time series data renders difficult to estimate the neat flow of information from each driver variable to a given target. We show that adopting an unnormalized definition of Granger causality one may put in evidence redundant multiplets of variables influencing the target by maximizing the total Granger causality to a given target, over all the possible partitions of the set of driving variables. Consequently we introduce a pairwise index of synergy which is zero when two independent sources additively influence the future state of the system, differently from previous definitions of synergy. Results: We report the application of the proposed approach to resting state fMRI data from the Human Connectome Project, showing that redundant pairs of regions arise mainly due to space contiguity and interhemispheric symmetry, whilst synergy occurs mainly between non-homologous pairs of regions in opposite hemispheres. Conclusions: Redundancy and synergy, in healthy resting brains, display characteristic patterns, revealed by the proposed approach. Significance: The pairwise synergy index, here introduced, maps the informational character of the system at hand into a weighted complex network: the same approach can be applied to other complex systems whose normal state corresponds to a balance between redundant and synergetic circuits.Comment: 6 figures. arXiv admin note: text overlap with arXiv:1403.515

Human Missions Analysis for Intelligent Missions Improvement

The topic of this chapter is not manned vs. robotic missions but how to integrate them for successful missions. The point of view is from that of a human observer, an ethologist, with the goal to gain further knowledge on human behavior and high technology readiness levels in the field of exploration missions. On one hand, the concept is the adaptability of men/women to be trained and on the other hand, it is the reliability of artificial intelligence systems to be incremented. The content of the chapter is: (i) ethological analysis based on numerical methods; (ii) strategy, cooperation, and adaptation; (iii) artificial intelligence and emotional intelligence; and (iv) man-rated Mars exploration demands