This paper presents an active search trajectory synthesis technique for autonomous mobile robots with nonlinear measurements and dynamics. The presented approach uses the ergodicity of a planned trajectory with respect to an expected information density map to close the loop during search. The ergodic control algorithm does not rely on discretization of the search or action spaces, and is well posed for coverage with respect to the expected information density whether the information is diffuse or localized, thus trading off between exploration and exploitation in a single objective function. As a demonstration, we use a robotic electrolocation platform to estimate location and size parameters describing static targets in an underwater environment. Our results demonstrate that the ergodic exploration of distributed information (EEDI) algorithm outperforms commonly used information-oriented controllers, particularly when distractions are present.Comment: 17 page

This paper presents a new model-based algorithm that computes predictive optimal controls on-line and in closed loop for traditionally challenging nonlinear systems. Examples demonstrate the same algorithm controlling hybrid impulsive, underactuated, and constrained systems using only high-level models and trajectory goals. Rather than iteratively optimize finite horizon control sequences to minimize an objective, this paper derives a closed-form expression for individual control actions, i.e., control values that can be applied for short duration, that optimally improve a tracking objective over a long time horizon. Under mild assumptions, actions become linear feedback laws near equilibria that permit stability analysis and performance-based parameter selection. Globally, optimal actions are guaranteed existence and uniqueness. By sequencing these actions on-line, in receding horizon fashion, the proposed controller provides a min-max constrained response to state that avoids the overhead typically required to impose control constraints. Benchmark examples show the approach can avoid local minima and outperform nonlinear optimal controllers and recent, case-specific methods in terms of tracking performance, and at speeds orders of magnitude faster than traditionally achievable.3 are nonlinear in state x : R → R n . Though these methods apply more broadly, we derive controls for the case where (1) is linear with respect to the control, u : R → R m , satisfying control-affine form, f (t, x(t), u(t)) = g(t, x(t)) + h(t, x(t)) u(t) .(2)The time dependence in (1) and (2) will be dropped for brevity. The prediction phase simulates motion resulting from some choice of nominal control, u = u 1 . Thus, the nominal predicted motion corresponds to, u 1 (t)).Although the nominal control may be chosen arbitrarily, all examples here use a null nominal control, u 1 = 0. Hence, in the SLIP example, SAC seeks actions that improve performance relative to doing nothing, i.e., letting the SLIP fall.With l 1 : R n → R and m : R n → R, the cost functional,

The evolution of terrestrial vertebrates, starting around 385 million years ago, is an iconic moment in evolution that brings to mind images of fish transforming into four-legged animals. Here, we show that this radical change in body shape was preceded by an equally dramatic change in sensory abilities akin to transitioning from seeing over short distances in a dense fog to seeing over long distances on a clear day. Measurements of eye sockets and simulations of their evolution show that eyes nearly tripled in size just before vertebrates began living on land. Computational simulations of these animal's visual ecology show that for viewing objects through water, the increase in eye size provided a negligible increase in performance. However, when viewing objects through air, the increase in eye size provided a large increase in performance. The jump in eye size was, therefore, unlikely to have arisen for seeing through water and instead points to an unexpected hybrid of seeing through air while still primarily inhabiting water. Our results and several anatomical innovations arising at the same time suggest lifestyle similarity to crocodiles. The consequent combination of the increase in eye size and vision through air would have conferred a 1 million-fold increase in the amount of space within which objects could be seen. The "buena vista" hypothesis that our data suggest is that seeing opportunities from afar played a role in the subsequent evolution of fully terrestrial limbs as well as the emergence of elaborated action sequences through planning circuits in the nervous system. fish-tetrapod transition | vision | visual ecology | terrestriality | prospective cognition B efore terrestrial vertebrates arose, their ancestors inhabited underwater environments, where vision is highly compromised compared with vision above water. The visual difference between life in water and life above it is comparable with driving fast on a foggy road, where our responses must be rapid and simple, vs. driving in clear daylight conditions, where deliberation over more complex choices is enabled by the vast increase in sensory range. Nonetheless, although an immense quantity of work has been done on the emergence of limbs during the evolution of land vertebrates, how visual capability changed during the transition from water to land has not been explored. In part, this lack of exploration is because computational visual ecology-necessary to interpret the fossil data-has not been combined with early tetrapod paleontology. Through combining these disciplines, here we probe the evolutionary history of the switch in our visual sensory ecology from water to air. Surprisingly, our results show that eyes tripled in size just before full-time life on land evolved. Convergent lines of evidence, including our own, strongly support the hypothesis that a crocodilian ecotype-using the greatly enhanced visual capabilities conferred by vision through air to prey on the bounty of unexploited invertebrates that long preceded the vertebrates onto ...

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This paper presents an active learning strategy for robotic systems that takes into account task information, enables fast learning, and allows control to be readily synthesized by taking advantage of the Koopman operator representation. We first motivate the use of representing nonlinear systems as linear Koopman operator systems by illustrating the improved model-based control performance with an actuated Van der Pol system. Informationtheoretic methods are then applied to the Koopman operator formulation of dynamical systems where we derive a controller for active learning of robot dynamics. The active learning controller is shown to increase the rate of information about the Koopman operator. In addition, our active learning controller can readily incorporate policies built on the Koopman dynamics, enabling the benefits of fast active learning and improved control. Results using a quadcopter illustrate single-execution active learning and stabilization capabilities during free fall. The results for active learning are extended for automating Koopman observables and we implement our method on real robotic systems.

Previous studies using magnetic resonance imaging (MRI) have revealed widespread fatty infiltrates in the neck extensor 5 and flexor 8 muscles of individuals with chronic whiplash-associated disorders (WADs). These high levels of muscle fat infiltration (MFI) were not present in those with chronic nontraumatic neck pain 6 or those without a history of neck disorders.5 While widespread, the greatest magnitude of MFI was consistently observed in the deepest muscular layer of the extensors (eg, the multifidus and semispinalis cervicis) when compared to the more superficial musculature (eg, semispinalis capitis, splenius capitis, and upper trapezius). 5,9The specific role of MFI in the development and maintenance of chronic WAD is not fully understood.7,24 Improvements in our mechanistic understanding of the development of structural changes (eg, composition and morphology) in the cervical muscles of patients with chronic WAD may shed light on their potential contribution to poor functional recovery. T T STUDY DESIGN:Cross-sectional. T T OBJECTIVES:To quantify the magnitude and distribution of muscle fat infiltration (MFI) within the cervical multifidus and semispinalis cervicis muscles in participants with chronic whiplashassociated disorders (WADs) compared to those who have fully recovered from a whiplash injury and healthy controls. T T BACKGROUND: Previous research has estab-lished the presence of increased MFI throughout the cervical extensor muscles of individuals with WAD when compared to healthy controls. These changes appear to be greater in the deepest muscles (eg, multifidus and semispinalis cervicis) than in the more superficial muscles. A detailed analysis of the distribution of MFI within these deep extensor muscles in chronic WAD, recovered, and control groups would provide a foundation for further investigation of specific mechanisms, etiologies, and targets for treatments. T T METHODS:Fifteen participants (WAD, n = 5; recovered, n = 5; and control, n = 5) were studied using a 3-D fat-water separation magnetic resonance imaging sequence. Bilateral measures of cervical multifidus and semispinalis cervicis MFI in 4 quartiles (1 [medial] to 4 [lateral]) at cervical levels C3 through C7 were included in the analysis. Intrarater and interrater reliability were established. A mixed-model analysis was performed to control for covariates, identify interaction effects, and compare MFI distribution between groups. T T RESULTS:The limits of agreement confirmed strong intrarater and interrater agreement at all levels (C3-C7). Sex, age, and body mass index were identified as significant covariates for MFI. Significant interactions were found between group and muscle quartile (P<.001) and between muscle quartile and cervical level (P<.001). Pairwise comparisons for intraquartile MFI between groups revealed significantly greater MFI in the WAD group when compared to the recovered group in the first quartile (P<.001), second quartile (P<.001), and third quartile (P = .03). When compared to the control group, the WA...

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Abstract-Although a number of solutions exist for the problems of coverage, search and target localization-commonly addressed separately-whether there exists a unified strategy that addresses these objectives in a coherent manner without being application-specific remains a largely open research question. In this paper, we develop a receding-horizon ergodic control approach, based on hybrid systems theory, that has the potential to fill this gap. The nonlinear model predictive control algorithm plans real-time motions that optimally improve ergodicity with respect to a distribution defined by the expected information density across the sensing domain. We establish a theoretical framework for global stability guarantees with respect to a distribution. Moreover, the approach is distributable across multiple agents, so that each agent can independently compute its own control while sharing statistics of its coverage across a communication network. We demonstrate the method in both simulation and in experiment in the context of target localization, illustrating that the algorithm is independent of the number of targets being tracked and can be run in real-time on computationally limited hardware platforms.