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Proceedings ArticleDOI

Integrating neuroscience-based models towards an autonomous biomimetic Synthetic Forager

TL;DR: This proposal is built upon the well-established Distributed Adaptive Control (DAC) framework and brings together neuroscience-based models of decision-making, multi-modal sensory processing, localization and mapping and allostatic behavioral control into one general autonomous robot controller.
Abstract: Foraging can be described as goal-oriented exploration for resources. It exemplifies how animals coordinate complex sensory and effector systems under varying environmental conditions. To emulate the foraging capabilities of natural systems is a major goal for robotics. Therefore, foraging is an excellent paradigm to benchmark novel autonomous control strategies. Here we describe the biomimetic control architecture of the Synthetic Forager (SF), an effort to integrate multiple biologically constrained models of specific perceptual and cognitive processes pertaining to foraging into one general autonomous robot controller. This proposal is built upon the well-established Distributed Adaptive Control (DAC) framework and brings together neuroscience-based models of decision-making, multi-modal sensory processing, localization and mapping and allostatic behavioral control. To show the potential of the SF model we used it to control a high-mobility wheeled robotic platform in three behavioral tasks similar to experimental protocols applied to rodents. We show that the robot can reliably perform cue detection, rule learning and goal-oriented navigation in open environments. We propose that this approach to robotics allows both the study of embodied neuroscience models and the transfer of brain based principles to robotic systems.
Citations
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01 Jan 2010
TL;DR: From motor to interaction learning in robots, this paper presented a workshop at the IEEE/RSJ International Conference on Intelligent Robot Systems (ICIS), which was largely based on the successful workshop From motor-to-interaction-learning in robots.
Abstract: From an engineering standpoint, the increasing complexity of robotic systems and the increasing demand for more autonomously learning robots, has become essential. This book is largely based on the successful workshop From motor to interaction learning in robots held at the IEEE/RSJ International Conference on Intelligent Robot Systems. The major aim of the book is to give students interested the topics described above a chance to get started faster and researchers a helpful compandium.

91 citations

Journal ArticleDOI
01 Jul 2012
TL;DR: This review will focus on the systems involved in realizing the core principles underlying the reactive layer: the allostatic control of fundamental behavior systems in the vertebrate brain and the emergent non-linearity through neuronal mass action in the locust brain.
Abstract: Distributed Adaptive Control (DAC) is a theory of the design principles underlying the Mind, Brain, Body Nexus (MBBN) that has been developed over the last 20 years. DAC assumes that the brain maintains stability between an embodied agent and its environment through action. It postulates that in order to act, or know how, the brain has to answer four fundamental questions: why, what, where, when. Thus the function of the brain is to continuously solve the, so called, H4W problem. The DAC theory is expressed as a robot based neural architecture organized in two complementary structures: layers and columns. The organizational layers are called: reactive, adaptive and contextual and its columnar organization defines the processing of states of the world, the self and the generation of action. Each layer is described with respect to its key hypotheses, implementation and specific benchmarks. After this overview of the key elements of DAC, the mapping of its key assumptions towards the invertebrate and mammalian brain is described. In particular, this review will focus on the systems involved in realizing the core principles underlying the reactive layer: the allostatic control of fundamental behavior systems in the vertebrate brain and the emergent non-linearity through neuronal mass action in the locust brain. The adaptive layer will be analyzed in terms of the classical conditioning paradigm and its neuronal substrate the amygdala-cerebellum-neocortex complex together with episodic memory and the formation of sense-act couplets in the hippocampus. For the contextual layer the ability of circuits in the prefrontal cortex to acquire and express contextual plans for action is described. The general overview of DAC’s explanation of MBBN is combined by examples of application scenarios in which DAC has been validated including mobile and humanoid robots, neurorehabilitation and the large-scale interactive space Ada. After 20 years of research DAC can be considered a mature theory of MBBN. It has build up a track record of explaining core aspects of mind, brain and behavior, has made testable and verified predictions at the level of behavior, physiology and anatomy, has been shown to be able to control complex real-world artefacts and has been successfully applied to brain repair and neurorehabilitation. Currently DAC is extended to capture the phenomenon of consciousness, the ultimate challenge in the study of the Mind, Brain, Body Nexus.

76 citations


Cites methods from "Integrating neuroscience-based mode..."

  • ...As an integration platform DAC is focussing on both robust outdoor mobile robot platforms to validate neuroDAC in rodent like foraging tasks (Rennó-Costa et al., 2011) and on the humanoid robot iCub to generalize DAC towards social interaction (Luvizotto, Rennó-Costa, Pattacini, & Verschure,…...

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Journal ArticleDOI
TL;DR: A review of the current state of the art in conventional and biomimetic goal-directed navigation models, focusing on the key principles of navigation in real-world environments including uncertainty in sensing, landmark observation and world modelling, can be found in this article.
Abstract: Mobile robots and animals alike must effectively navigate their environments in order to achieve their goals. For animals goal-directed navigation facilitates finding food, seeking shelter or migration; similarly robots perform goal-directed navigation to find a charging station, get out of the rain or guide a person to a destination. This similarity in tasks extends to the environment as well; increasingly, mobile robots are operating in the same underwater, ground and aerial environments that animals do. Yet despite these similarities, goal-directed navigation research in robotics and biology has proceeded largely in parallel, linked only by a small amount of interdisciplinary research spanning both areas. Most state-of-the-art robotic navigation systems employ a range of sensors, world representations and navigation algorithms that seem far removed from what we know of how animals navigate; their navigation systems are shaped by key principles of navigation in ‘real-world’ environments including dealing with uncertainty in sensing, landmark observation and world modelling. By contrast, biomimetic animal navigation models produce plausible animal navigation behaviour in a range of laboratory experimental navigation paradigms, typically without addressing many of these robotic navigation principles. In this paper, we attempt to link robotics and biology by reviewing the current state of the art in conventional and biomimetic goal-directed navigation models, focusing on the key principles of goal-oriented robotic navigation and the extent to which these principles have been adapted by biomimetic navigation models and why.

29 citations

Book ChapterDOI
09 Jul 2012
TL;DR: An autonomous waiter robot control system based on the reactive layer of the Distributed Adaptive Control (DAC) architecture that has to explore the space where catering is set and invite the guests to serve themselves with chocolate or candies is presented.
Abstract: We present an autonomous waiter robot control system based on the reactive layer of the Distributed Adaptive Control (DAC) architecture. The waiterbot has to explore the space where catering is set and invite the guests to serve themselves with chocolate or candies. The control model is taking advantage of DAC’s allostatic control system that allows the selection of actions through the modulation of drive states. In the robot´s control system two independent behavioral loops are implemented serving specific goals: a navigation system to explore the space and a gazing behavior that invites human users to serve themselves. By approaching and gazing at a potential consumer the robot performs its serving behavior. The system was tested in a simulated environment and during a public event where it successfully delivered its wares. From the observed interactions the effect of drive based self-regulated action in living machines is discussed.

1 citations

Book ChapterDOI
09 Jul 2012
TL;DR: This paper combines the canonical cortical computational principle of the TPC model with two other systems: an attention system and a hippocampus model, and suggests that TPC can be efficiently used in a high complexity task such as robot navigation.
Abstract: Recently, we have proposed that the dense local and sparse long-range connectivity of the visual cortex accounts for the rapid and robust transformation of visual stimulus information into a temporal population code, or TPC. In this paper, we combine the canonical cortical computational principle of the TPC model with two other systems: an attention system and a hippocampus model. We evaluate whether the TPC encoding strategy can be efficiently used to generate a spatial representation of the environment. We benchmark our architecture using stimulus input from a real-world environment. We show that the mean correlation of the TPC representation in two different positions of the environment has a direct relationship with the distance between these locations. Furthermore, we show that this representation can lead to the formation of place cells. Our results suggest that TPC can be efficiently used in a high complexity task such as robot navigation.

1 citations


Cites methods from "Integrating neuroscience-based mode..."

  • ...The images used in the experiments are acquired in a real-world environment taken by the camera of a mobile robot called the Synthetic Forager (SF) [18]....

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  • ...The images were acquired using the SF-robot [18]....

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References
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Proceedings ArticleDOI
20 Sep 1999
TL;DR: Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.
Abstract: An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients in multiple orientation planes and at multiple scales. The keys are used as input to a nearest neighbor indexing method that identifies candidate object matches. Final verification of each match is achieved by finding a low residual least squares solution for the unknown model parameters. Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.

16,989 citations


"Integrating neuroscience-based mode..." refers methods in this paper

  • ...In SF we use the SIFT algorithm [23]....

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Journal ArticleDOI
TL;DR: Preliminary observations on the behaviour of hippocampusal units in the freely moving rat provide support for this theory of hippocampal function.

5,549 citations

Journal ArticleDOI
TL;DR: Experiments on single, multiple, and concurrent schedules of reinforcement find various correlations between the rate of responding and the rate or magnitude of reinforcement, which can be accounted for by a coherent system of equations.
Abstract: Experiments on single, multiple, and concurrent schedules of reinforcement find various correlations between the rate of responding and the rate or magnitude of reinforcement. For concurrent schedules (i.e., simultaneous choice procedures), there is matching between the relative frequencies of responding and reinforcement; for multiple schedules (i.e., successive discrimination procedures), there are contrast effects between responding in each component and reinforcement in the others; and for single schedules, there are a host of increasing monotonic relations between the rate of responding and the rate of reinforcement. All these results, plus several others, can be accounted for by a coherent system of equations, the most general of which states that the absolute rate of any response is proportional to its associated relative reinforcement.

2,690 citations


"Integrating neuroscience-based mode..." refers background in this paper

  • ...For example, it has been shown that rats choose their strategies based on the expected gain and its magnitude [10]....

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Journal ArticleDOI
16 Feb 2007-Science
TL;DR: These results imply a dual mechanism for pattern separation in which signals from the entorhinal cortex can be decorrelated both by changes in coincidence patterns in the dentate gyrus and by recruitment of nonoverlapping cell assemblies in CA3.
Abstract: Theoretical models have long pointed to the dentate gyrus as a possible source of neuronal pattern separation. In agreement with predictions from these models, we show that minimal changes in the shape of the environment in which rats are exploring can substantially alter correlated activity patterns among place-modulated granule cells in the dentate gyrus. When the environments are made more different, new cell populations are recruited in CA3 but not in the dentate gyrus. These results imply a dual mechanism for pattern separation in which signals from the entorhinal cortex can be decorrelated both by changes in coincidence patterns in the dentate gyrus and by recruitment of nonoverlapping cell assemblies in CA3.

1,503 citations

MonographDOI
01 Jan 1963

929 citations


"Integrating neuroscience-based mode..." refers background in this paper

  • ...In nature, abundant examples of biological systems exist that fulfill all these requirements, sometimes in the most unpredictable and challenging ecosystems [1]....

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