MUVTIME: a Multivariate time series visualizer for behavioral science

As behavioral science becomes progressively more data driven, the need is increasing for appropriate tools for visual exploration and analysis of large datasets, often formed by multivariate time series. This paper describes MUVTIME, a multimodal time series visualization tool, developed in Matlab that allows a user to load a time series collection (a multivariate time series dataset) and an associated video. The user can plot several time series on MUVTIME and use one of them to do brushing on the displayed data, i.e. select a time range dynamically and have it updated on the display. The tool also features a categorical visualization of two binary time series that works as a high-level descriptor of the coordination between two interacting partners. The paper reports the successful use of MUVTIME under the scope of project TURNTAKE, which was intended to contribute to the improvement of human-robot interaction systems by studying turn- taking dynamics (role interchange) in parent-child dyads during joint action.Marie Curie International Incoming Fellowship PIIF-GA-2011- 301155; Portuguese Foundation for Science and Technology (FCT) project PTDC/PSI- PCO/121494/2010; AFP was also partially funded by the FCT project (IF/00217/2013)This research was supported by: Marie Curie International Incoming Fellowship PIIF-GA-2011301155; Portuguese Foundation for Science and Technology (FCT) Strategic program FCT UID/EEA/00066/2013; FCT project PTDC/PSIPCO/121494/2010. AFP was also partially funded by the FCT project (IF/00217/2013). REFERENCE

A software framework for the implementation of dynamic neural field control architectures for human-robot interaction

Useful and efficient human-robot interaction in joint tasks requires the design of a cognitive control architecture that endows robots with crucial cognitive and social capabilities such as intention recognition and complementary action selection. Herein, we present a software framework that eases the design and implementation of Dynamic Neural Field (DNF) cognitive architectures for human-robot joint tasks. We provide a graphical user interface to draw instances of the robot's control architecture. In addition, it allows to simulate, inspect and parametrize them in real-time. The framework eases parameter tuning by allowing changes on-the-fly and by connecting the cognitive architecture with simulated or real robots. Using the case study of an anthropomorphic robot providing assistance to a disabled person during a meal scenario, we illustrate the applicability of the framework.The work was funded by Project NETT: Neural Engineering Transformative Technologies, EU-FP7 ITN (nr.289146), and by FCT - Fundação para a Ciência e Tecnologia, through the Phd and Posdoc Grants (SFRH/BD/81334/2011 and SFRH/BPD/71874/2010 respectively, financed by POPH-QREN-Type 4.1- Advanced Training, co-funded by the European Social Fund and national funds from MEC), and Project Scope: UID/CEC/00319/2013 together with COMPETE: POCI-01-0145-FEDER007043.info:eu-repo/semantics/publishedVersio

A safe autonomous stacker in human shared workspaces

This paper proposes a solution for safe navigation of stacker vehicles in workspaces shared with people, with a focus on the docking manoeuvres for pallet picking and dropping. Behaviours for way-point and wall following are developed following the attractor dynamics approach. Then, these behaviours are orchestrated by state machines (that activate or deactivate them) depending on the specific task. Each of these states also defines different safe areas and maximum travel speeds, which is a requirement for safe operation. Results of real experiments are presented that show the standard operation and its robustness against perturbations (people in the way) and failure detection (missing pallets).Fundação para a Ciência e Tecnologia (FCT) within the R&D Units Project Scope: UIDB/00319/202

Transportation of long objects in unknown cluttered environments by a team of robots: a dynamical systems approach

We present a distributed architecture for teams of two autonomous mobile robots that act in coordination in a joint transportation task of long objects. The team is able to perform its transportation task in unknown environments while avoiding static or moving obstacles. The working environment can be cluttered and with narrow passages such as corridors, corners and doors. These characteristics make our approach suitable to be deployed in warehouses or office-like environments. The control architecture of each robot is formalized as a non-linear dynamical system, where by design attractor states dominate. The overt behavior is smooth and stable, because it is generated as a time sequence of attractor states, for the control variables, which contributes to the overall asymptotically stability of the system that makes it robust against perturbations. We present results with real robots in a real indoor cluttered environment.This work is also supported by FEDER Funds through Competitivity Factors Operational Program - COMPETE and National Funds by FCT Portuguese Science and Technology Foundation under the Project FCOMP-01-0124-FEDER022674. We would like to thank all the people that work in our laboratory, MARL (Mobile and Anthropomorphic Robotics Laboratory) at University of Minho. All of them contributed in several ways for the success of this work. Toni Machado would also like to thank the Portuguese Science and Technology Foundation for providing his Ph.D. scholarship (ref. SFRH/BD/38885/2007)