13,336 research outputs found
The Mechanics of Embodiment: A Dialogue on Embodiment and Computational Modeling
Embodied theories are increasingly challenging traditional views of cognition by arguing that conceptual representations that constitute our knowledge are grounded in sensory and motor experiences, and processed at this sensorimotor level, rather than being represented and processed abstractly in an amodal conceptual system. Given the established empirical foundation, and the relatively underspecified theories to date, many researchers are extremely interested in embodied cognition but are clamouring for more mechanistic implementations. What is needed at this stage is a push toward explicit computational models that implement sensory-motor grounding as intrinsic to cognitive processes. In this article, six authors from varying backgrounds and approaches address issues concerning the construction of embodied computational models, and illustrate what they view as the critical current and next steps toward mechanistic theories of embodiment. The first part has the form of a dialogue between two fictional characters: Ernest, the �experimenter�, and Mary, the �computational modeller�. The dialogue consists of an interactive sequence of questions, requests for clarification, challenges, and (tentative) answers, and touches the most important aspects of grounded theories that should inform computational modeling and, conversely, the impact that computational modeling could have on embodied theories. The second part of the article discusses the most important open challenges for embodied computational modelling
Sentiment Analysis for Words and Fiction Characters From The Perspective of Computational (Neuro-)Poetics
Two computational studies provide different sentiment analyses for text segments (e.g., ‘fearful’ passages) and figures (e.g., ‘Voldemort’) from the Harry Potter books (Rowling, 1997 - 2007) based on a novel simple tool called SentiArt. The tool uses vector space models together with theory-guided, empirically validated label lists to compute the valence of each word in a text by locating its position in a 2d emotion potential space spanned by the > 2 million words of the vector space model. After testing the tool’s accuracy with empirical data from a neurocognitive study, it was applied to compute emotional figure profiles and personality figure profiles (inspired by the so-called ‚big five’ personality theory) for main characters from the book series. The results of comparative analyses using different machine-learning classifiers (e.g., AdaBoost, Neural Net) show that SentiArt performs very well in predicting the emotion potential of text passages. It also produces plausible predictions regarding the emotional and personality profile of fiction characters which are correctly identified on the basis of eight character features, and it achieves a good cross-validation accuracy in classifying 100 figures into ‘good’ vs. ‘bad’ ones. The results are discussed with regard to potential applications of SentiArt in digital literary, applied reading and neurocognitive poetics studies such as the quantification of the hybrid hero potential of figures
Self-Sensing Control for Soft-Material Actuators Based on Dielectric Elastomers
Due to their energy density and softness that are comparable to human muscles dielectric elastomer (DE) transducers are well-suited for soft-robotic applications. This kind of transducer combines actuator and sensor functionality within one transducer so that no external senors to measure the deformation or to detect collisions are required. Within this contribution we present a novel self-sensing control for a DE stack-transducer that allows to control several different quantities of the DE transducer with the same controller. This flexibility is advantageous e.g., for the development of human machine interfaces with soft-bodied robots. After introducing the DE stack-transducer that is driven by a bidirectional flyback converter, the development of the self-sensing state and disturbance estimator based on an extended Kalman-filter is explained. Compared to known estimators designed for DE transducers supplied by bulky high-voltage amplifiers this one does not require any superimposed excitation to enable the sensor capability so that it also can be used with economic and competitive power electronics like the flyback converter. Due to the behavior of this converter a sliding mode energy controller is designed afterwards. By introducing different feed-forward controls the voltage, force or deformation can be controlled. The validation proofs that both the developed self-sensing estimator as well as the self-sensing control yield comparable results as previously published sensor-based approaches.TU Berlin, Open-Access-Mittel - 201
RoboCup 2D Soccer Simulation League: Evaluation Challenges
We summarise the results of RoboCup 2D Soccer Simulation League in 2016
(Leipzig), including the main competition and the evaluation round. The
evaluation round held in Leipzig confirmed the strength of RoboCup-2015
champion (WrightEagle, i.e. WE2015) in the League, with only eventual finalists
of 2016 competition capable of defeating WE2015. An extended, post-Leipzig,
round-robin tournament which included the top 8 teams of 2016, as well as
WE2015, with over 1000 games played for each pair, placed WE2015 third behind
the champion team (Gliders2016) and the runner-up (HELIOS2016). This
establishes WE2015 as a stable benchmark for the 2D Simulation League. We then
contrast two ranking methods and suggest two options for future evaluation
challenges. The first one, "The Champions Simulation League", is proposed to
include 6 previous champions, directly competing against each other in a
round-robin tournament, with the view to systematically trace the advancements
in the League. The second proposal, "The Global Challenge", is aimed to
increase the realism of the environmental conditions during the simulated
games, by simulating specific features of different participating countries.Comment: 12 pages, RoboCup-2017, Nagoya, Japan, July 201
A Playful Experiential Learning System With Educational Robotics
This article reports on two studies that aimed to evaluate the effective impact of
educational robotics in learning concepts related to Physics and Geography. The
reported studies involved two courses from an upper secondary school and two courses
froma lower secondary school. Upper secondary school classes studied topics ofmotion
physics, and lower secondary school classes explored issues related to geography.
In each grade, there was an “experimental group” that carried out their study using
robotics and cooperative learning and a “control group” that studied the same concepts
without robots. Students in both classes were subjected to tests before and after the
robotics laboratory, to check their knowledge in the topics covered. Our initial hypothesis
was that classes involving educational robotics and cooperative learning are more
effective in improving learning and stimulating the interest and motivation of students.
As expected, the results showed that students in the experimental groups had a far
better understanding of concepts and higher participation to the activities than students
in the control groups
Autonomous 3D Exploration of Large Structures Using an UAV Equipped with a 2D LIDAR
This paper addressed the challenge of exploring large, unknown, and unstructured
industrial environments with an unmanned aerial vehicle (UAV). The resulting system combined
well-known components and techniques with a new manoeuvre to use a low-cost 2D laser to measure
a 3D structure. Our approach combined frontier-based exploration, the Lazy Theta* path planner, and
a flyby sampling manoeuvre to create a 3D map of large scenarios. One of the novelties of our system
is that all the algorithms relied on the multi-resolution of the octomap for the world representation.
We used a Hardware-in-the-Loop (HitL) simulation environment to collect accurate measurements
of the capability of the open-source system to run online and on-board the UAV in real-time. Our
approach is compared to different reference heuristics under this simulation environment showing
better performance in regards to the amount of explored space. With the proposed approach, the UAV
is able to explore 93% of the search space under 30 min, generating a path without repetition that
adjusts to the occupied space covering indoor locations, irregular structures, and suspended obstaclesUnión Europea Marie Sklodowska-Curie 64215Unión Europea MULTIDRONE (H2020-ICT-731667)Uniión Europea HYFLIERS (H2020-ICT-779411
Acceptability of the transitional wearable companion “+me” in typical children: a pilot study
This work presents the results of the first experimentation of +me-the first prototype of
Transitional Wearable Companion–run on 15 typically developed (TD) children with ages
between 8 and 34 months. +me is an interactive device that looks like a teddy bear that
can be worn around the neck, has touch sensors, can emit appealing lights and sounds,
and has input-output contingencies that can be regulated with a tablet via Bluetooth.
The participants were engaged in social play activities involving both the device and
an adult experimenter. +me was designed with the objective of exploiting its intrinsic
allure as an attractive toy to stimulate social interactions (e.g., eye contact, turn taking,
imitation, social smiles), an aspect potentially helpful in the therapy of Autism Spectrum
Disorders (ASD) and other Pervasive Developmental Disorders (PDD). The main purpose
of this preliminary study is to evaluate the general acceptability of the toy by TD children,
observing the elicited behaviors in preparation for future experiments involving children
with ASD and other PDD. First observations, based on video recording and scoring,
show that +me stimulates good social engagement in TD children, especially when their
age is higher than 24 months
Embodied Evolution in Collective Robotics: A Review
This paper provides an overview of evolutionary robotics techniques applied
to on-line distributed evolution for robot collectives -- namely, embodied
evolution. It provides a definition of embodied evolution as well as a thorough
description of the underlying concepts and mechanisms. The paper also presents
a comprehensive summary of research published in the field since its inception
(1999-2017), providing various perspectives to identify the major trends. In
particular, we identify a shift from considering embodied evolution as a
parallel search method within small robot collectives (fewer than 10 robots) to
embodied evolution as an on-line distributed learning method for designing
collective behaviours in swarm-like collectives. The paper concludes with a
discussion of applications and open questions, providing a milestone for past
and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl
EEG theta and Mu oscillations during perception of human and robot actions.
The perception of others' actions supports important skills such as communication, intention understanding, and empathy. Are mechanisms of action processing in the human brain specifically tuned to process biological agents? Humanoid robots can perform recognizable actions, but can look and move differently from humans, and as such, can be used in experiments to address such questions. Here, we recorded EEG as participants viewed actions performed by three agents. In the Human condition, the agent had biological appearance and motion. The other two conditions featured a state-of-the-art robot in two different appearances: Android, which had biological appearance but mechanical motion, and Robot, which had mechanical appearance and motion. We explored whether sensorimotor mu (8-13 Hz) and frontal theta (4-8 Hz) activity exhibited selectivity for biological entities, in particular for whether the visual appearance and/or the motion of the observed agent was biological. Sensorimotor mu suppression has been linked to the motor simulation aspect of action processing (and the human mirror neuron system, MNS), and frontal theta to semantic and memory-related aspects. For all three agents, action observation induced significant attenuation in the power of mu oscillations, with no difference between agents. Thus, mu suppression, considered an index of MNS activity, does not appear to be selective for biological agents. Observation of the Robot resulted in greater frontal theta activity compared to the Android and the Human, whereas the latter two did not differ from each other. Frontal theta thus appears to be sensitive to visual appearance, suggesting agents that are not sufficiently biological in appearance may result in greater memory processing demands for the observer. Studies combining robotics and neuroscience such as this one can allow us to explore neural basis of action processing on the one hand, and inform the design of social robots on the other
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