La dynamique des attracteurs comme base de génération de comportements en robotique mobile autonome

L’hypothèse centrale de l’approche dynamique en robotique mobile autonome (Schöner et Dose, 1992 ; Schöner, Dose et Engels, 1995 ; Bicho et Schöner, 1997 ; Steinhage et Schöner, 1998 ; Large, Christensen et Bajcy, 1999 ; Bicho, Mallet et Schöner, 2000) est que le comportement moteur ainsi que les représentations pertinentes nécessaires à sa réalisation doivent, d’une part, être générés de façon continue dans le temps, et, d’autre part, résister aux fluctuations ou perturbations auxquelles tout système réel est exposé. Cela conduit à une conception dans laquelle le comportement et les représentations sont les solutions stables (ou attracteurs) d’un ensemble de systèmes dynamiques, qui traduisent en temps réel l’information sensorielle en contraintes graduées et intégrables. De multiples attracteurs peuvent co-exister en présence de la même situation sensorielle. C’est l’état interne du système autonome qui décidera quel attracteur sera choisi. Le changement du nombre et/ou de la nature des attracteurs à travers des instabilités (ou bifurcations) permet au système autonome de se configurer de manière flexible selon le contexte sensoriel instantané. L’approche est ici présentée au niveau de la génération de comportements moteurs. Dans ce cas, des variables « comportementales » représentent directement un continuum d’états physiques du système qui sont générés par des systèmes de contrôle conventionnels. Par exemple, une variable représentant la direction dans laquelle un véhicule se déplace, peut évoluer dans le temps grâce à un système dynamique qui intègre les contraintes « acquisition de cibles » et « évitement d’obstacles ». La fusion ou sélection parmi ces contraintes est réalisée au moyen d’une dynamique non linéaire bien maîtrisée

Linkage of cave-ice changes to weather patterns inside and outside the cave Eisriesenwelt (Tennengebirge, Austria)

The behaviour of perennial ice masses in karst caves in relation to the outside climate is still not well understood, though a significant potential of the cave-ice for paleo-climate reconstructions could be expected. This study investigates the relationship between weather patterns inside and outside the cave Eisriesenwelt (Austrian Alps) and ice-surface changes of the ice-covered part of the cave from meteorological observations at three sites (outside the cave, entrance-near inside and in the middle section of the cave) including atmospheric and ice surface measurements as well as an ablation stake network. Whereas ice loss in summer was a general feature from stake measurements for almost all measurement sites in the cave in 2007, 2008 and 2009 (values up to −15 cm yr<sup>−1</sup>), a clear seasonal signal of ice accumulation (e.g. in spring as expected from theory) was not observed. It is shown that under recent climate the cave ice mass balance is more sensitive to winter climate for the inner measurement site and sensitive to winter and summer climate for the entrance-near site. Observed ice surface changes can be well explained by cave atmosphere measurements, indicating a clear annual cycle with weak mass loss in winter due to sublimation, stable ice conditions in spring until summer (autumn for the inner measurement site) and significant melt in late summer to autumn (for the entrance-near site). Interestingly, surface ice melt did not contribute to ablation at the inner site. It is obvious from the spatial sample of ice surface height observations that the ice body is currently in rather balanced state, though the influence of show-cave management on ice mass-balance could not be clearly quantified (but a significant input on accumulation for some parts of the cave is rather plausible)

Towards endowing collaborative robots with fast learning for minimizing tutors’ demonstrations: what and when to do?

Programming by demonstration allows non-experts in robot programming to train the robots in an intuitive manner. However, this learning paradigm requires multiple demonstrations of the same task, which can be time-consuming and annoying for the human tutor. To overcome this limitation, we propose a fast learning system – based on neural dynamics – that permits collaborative robots to memorize sequential information from single task demonstrations by a human-tutor. Important, the learning system allows not only to memorize long sequences of sub-goals in a task but also the time interval between them. We implement this learning system in Sawyer (a collaborative robot from Rethink Robotics) and test it in a construction task, where the robot observes several human-tutors with different preferences on the sequential order to perform the task and different behavioral time scales. After learning, memory recall (of what and when to do a sub-task) allows the robot to instruct inexperienced human workers, in a particular human-centered task scenario.POFC - Programa Operacional Temático Factores de Competitividade(POCI-01-0247-FEDER-024541

Climate Changes and Their Elevational Patterns in the Mountains of the World

Quantifying rates of climate change in mountain regions is of considerable interest, not least because mountains are viewed as climate “hotspots” where change can anticipate or amplify what is occurring elsewhere. Accelerating mountain climate change has extensive environmental impacts, including depletion of snow/ice reserves, critical for the world's water supply. Whilst the concept of elevation-dependent warming (EDW), whereby warming rates are stratified by elevation, is widely accepted, no consistent EDW profile at the global scale has been identified. Past assessments have also neglected elevation-dependent changes in precipitation. In this comprehensive analysis, both in situ station temperature and precipitation data from mountain regions, and global gridded data sets (observations, reanalyses, and model hindcasts) are employed to examine the elevation dependency of temperature and precipitation changes since 1900. In situ observations in paired studies (using adjacent stations) show a tendency toward enhanced warming at higher elevations. However, when all mountain/lowland studies are pooled into two groups, no systematic difference in high versus low elevation group warming rates is found. Precipitation changes based on station data are inconsistent with no systematic contrast between mountain and lowland precipitation trends. Gridded data sets (CRU, GISTEMP, GPCC, ERA5, and CMIP5) show increased warming rates at higher elevations in some regions, but on a global scale there is no universal amplification of warming in mountains. Increases in mountain precipitation are weaker than for low elevations worldwide, meaning reduced elevation-dependency of precipitation, especially in midlatitudes. Agreement on elevation-dependent changes between gridded data sets is weak for temperature but stronger for precipitation

Universal neural field computation

Turing machines and G\"odel numbers are important pillars of the theory of computation. Thus, any computational architecture needs to show how it could relate to Turing machines and how stable implementations of Turing computation are possible. In this chapter, we implement universal Turing computation in a neural field environment. To this end, we employ the canonical symbologram representation of a Turing machine obtained from a G\"odel encoding of its symbolic repertoire and generalized shifts. The resulting nonlinear dynamical automaton (NDA) is a piecewise affine-linear map acting on the unit square that is partitioned into rectangular domains. Instead of looking at point dynamics in phase space, we then consider functional dynamics of probability distributions functions (p.d.f.s) over phase space. This is generally described by a Frobenius-Perron integral transformation that can be regarded as a neural field equation over the unit square as feature space of a dynamic field theory (DFT). Solving the Frobenius-Perron equation yields that uniform p.d.f.s with rectangular support are mapped onto uniform p.d.f.s with rectangular support, again. We call the resulting representation \emph{dynamic field automaton}.Comment: 21 pages; 6 figures. arXiv admin note: text overlap with arXiv:1204.546

Neural field model for measuring and reproducing time intervals

The continuous real-time motor interaction with our environment requires the capacity to measure and produce time intervals in a highly flexible manner. Recent neurophysiological evidence suggests that the neural computational principles supporting this capacity may be understood from a dynamical systems perspective: Inputs and initial conditions determine how a recurrent neural network evolves from a “resting state” to a state triggering the action. Here we test this hypothesis in a time measurement and time reproduction experiment using a model of a robust neural integrator based on the theoretical framework of dynamic neural fields. During measurement, the temporal accumulation of input leads to the evolution of a self-stabilized bump whose amplitude reflects elapsed time. During production, the stored information is used to reproduce on a trial-by-trial basis the time interval either by adjusting input strength or initial condition of the integrator. We discuss the impact of the results on our goal to endow autonomous robots with a human-like temporal cognition capacity for natural human-robot interactions.The work received financial support from FCT through the PhD fellowship PD/BD/128183/2016, the project ”Neurofield” (POCI-01-0145-FEDER-031393) and the research centre CMAT within the project UID/MAT/00013/2013

The Virtual Teacher (VT) Paradigm: Learning New Patterns of Interpersonal Coordination Using the Human Dynamic Clamp

The Virtual Teacher paradigm, a version of the Human Dynamic Clamp (HDC), is introduced into studies of learning patterns of inter-personal coordination. Combining mathematical modeling and experimentation, we investigate how the HDC may be used as a Virtual Teacher (VT) to help humans co-produce and internalize new inter-personal coordination pattern(s). Human learners produced rhythmic finger movements whilst observing a computer-driven avatar, animated by dynamic equations stemming from the well-established Haken-Kelso-Bunz (1985) and Schöner-Kelso (1988) models of coordination. We demonstrate that the VT is successful in shifting the pattern co-produced by the VT-human system toward any value (Experiment 1) and that the VT can help humans learn unstable relative phasing patterns (Experiment 2). Using transfer entropy, we find that information flow from one partner to the other increases when VT-human coordination loses stability. This suggests that variable joint performance may actually facilitate interaction, and in the long run learning. VT appears to be a promising tool for exploring basic learning processes involved in social interaction, unraveling the dynamics of information flow between interacting partners, and providing possible rehabilitation opportunities

Attractor dynamics approach to joint transportation by autonomous robots: theory, implementation and validation on the factory floor

This paper shows how non-linear attractor dynamics can be used to control teams of two autonomous mobile robots that coordinate their motion in order to transport large payloads in unknown environments, which might change over time and may include narrow passages, corners and sharp U-turns. Each robot generates its collision-free motion online as the sensed information changes. The control architecture for each robot is formalized as a non-linear dynamical system, where by design attractor states, i.e. asymptotically stable states, dominate and evolve over time. Implementation details are provided, and it is further shown that odometry or calibration errors are of no significance. Results demonstrate flexible and stable behavior in different circumstances: when the payload is of different sizes; when the layout of the environment changes from one run to another; when the environment is dynamice.g. following moving targets and avoiding moving obstacles; and when abrupt disturbances challenge team behavior during the execution of the joint transportation task.- This work was supported by FCT-Fundacao para a Ciencia e Tecnologia within the scope of the Project PEst-UID/CEC/00319/2013 and by the Ph.D. Grants SFRH/BD/38885/2007 and SFRH/BPD/71874/2010, as well as funding from FP6-IST2 EU-IP Project JAST (Proj. Nr. 003747). We would like to thank the anonymous reviewers, whose comments have contributed to improve the paper

The Problem of Signal and Symbol Integration: A Study of Cooperative Mobile Autonomous Agent Behaviors

This paper explores and reasons about the interplay between symbolic and continuous representations. We first provide some historical perspective on signal and symbol integration as viewed by the Artificial Intelligence (AI), Robotics and Computer Vision communities. The domain of autonomous robotic agents residing in dynamically changing environments anchors well different aspects of this integration and allows us to look at the problem in its entirety. Models of reasoning, sensing and control actions of such agents determine three different dimensions for discretization of the agent-world behavioral state space. The design and modeling of robotic agents, where these three aspects have to be closely tied together, provide a good experimental platform for addressing the signal-to-symbol transformation problem. We present some experimental results from the domain of cooperating mobile agents involved in tasks of navigation and manipulation