50 research outputs found
Peripersonal Space in the Humanoid Robot iCub
Developing behaviours for interaction with objects close to the body is a primary goal for any organism to survive in the world. Being able to develop such behaviours will be an essential feature in autonomous humanoid robots in order to improve their integration into human environments. Adaptable spatial abilities will make robots safer and improve their social skills, human-robot and robot-robot collaboration abilities.
This work investigated how a humanoid robot can explore and create action-based representations of its peripersonal space, the region immediately surrounding the body where reaching is possible without location displacement. It presents three empirical studies based on peripersonal space findings from psychology, neuroscience and robotics. The experiments used a visual perception system based on active-vision and biologically inspired neural networks.
The first study investigated the contribution of binocular vision in a reaching task. Results indicated the signal from vergence is a useful embodied depth estimation cue in the peripersonal space in humanoid robots. The second study explored the influence of morphology and postural experience on confidence levels in reaching assessment. Results showed that a decrease of confidence when assessing targets located farther from the body, possibly in accordance to errors in depth estimation from vergence for longer distances. Additionally, it was found that a proprioceptive arm-length signal extends the robot’s peripersonal space. The last experiment modelled development of the reaching skill by implementing motor synergies that progressively unlock degrees of freedom in the arm. The model was advantageous when compared to one that included no developmental stages.
The contribution to knowledge of this work is extending the research on biologically-inspired methods for building robots, presenting new ways to further investigate the robotic properties involved in the dynamical adaptation to body and sensing characteristics, vision-based action, morphology and confidence levels in reaching assessment.CONACyT, Mexico (National Council of Science and Technology
Estudio del impacto ambiental de las ondas lineales y no lineales generados por tsunamis y huracanes en zonas costeras
“El desarrollo de la teorĂa de ondas no lineales para aplicaciones hidrodinámicas es muy actual e importante para MĂ©xico, un paĂs con una lĂnea costera muy larga y una industria turĂstica desarrollada y con alta probabilidad de ser alcanzado por tsunamis locales, regionales o transoceánicos, asĂ como por huracanes de gran intensidad.
De ahĂ la importancia del estudio de procesos no lineales, como los tsunamis, mediante sistemas de ecuaciones diferenciales parciales no lineales como la ecuaciĂłn de Schördinger no lineal, para comprender su comportamiento, su aplicaciĂłn, asĂ como la interacciĂłn con el ambiente y contribuir con el discernimiento de la complejidad ambiental, puesto que en ella convergen diferentes miradas y lenguajes sobre lo real que se construyen a travĂ©s de epistemologĂas, racionalidades e imaginarios sobre la naturaleza.
Se desarrollĂł y aplico los mĂ©todos matemáticos para encontrar las soluciones analĂticas y numĂ©ricas de la NLSE generalizada y las propiedades de sus soluciones de tipo solitĂłn, asĂ como sus colisiones, propagaciĂłn y destrucciĂłn, para diseñar elementos que disminuyan el impacto destructivo de las olas producidas por huracanes y olas gigantescas o tsunamis
Leveraging Domain Knowledge for Inclusive and Bias-aware Humanitarian Response Entry Classification
Accurate and rapid situation analysis during humanitarian crises is critical
to delivering humanitarian aid efficiently and is fundamental to humanitarian
imperatives and the Leave No One Behind (LNOB) principle. This data analysis
can highly benefit from language processing systems, e.g., by classifying the
text data according to a humanitarian ontology. However, approaching this by
simply fine-tuning a generic large language model (LLM) involves considerable
practical and ethical issues, particularly the lack of effectiveness on
data-sparse and complex subdomains, and the encoding of societal biases and
unwanted associations. In this work, we aim to provide an effective and
ethically-aware system for humanitarian data analysis. We approach this by (1)
introducing a novel architecture adjusted to the humanitarian analysis
framework, (2) creating and releasing a novel humanitarian-specific LLM called
HumBert, and (3) proposing a systematic way to measure and mitigate biases. Our
experiments' results show the better performance of our approach on zero-shot
and full-training settings in comparison with strong baseline models, while
also revealing the existence of biases in the resulting LLMs. Utilizing a
targeted counterfactual data augmentation approach, we significantly reduce
these biases without compromising performance.Comment: Accepted at IJCAI 202
HumSet: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crisis Response
Timely and effective response to humanitarian crises requires quick and
accurate analysis of large amounts of text data - a process that can highly
benefit from expert-assisted NLP systems trained on validated and annotated
data in the humanitarian response domain. To enable creation of such NLP
systems, we introduce and release HumSet, a novel and rich multilingual dataset
of humanitarian response documents annotated by experts in the humanitarian
response community. The dataset provides documents in three languages (English,
French, Spanish) and covers a variety of humanitarian crises from 2018 to 2021
across the globe. For each document, HUMSET provides selected snippets
(entries) as well as assigned classes to each entry annotated using common
humanitarian information analysis frameworks. HUMSET also provides novel and
challenging entry extraction and multi-label entry classification tasks. In
this paper, we take a first step towards approaching these tasks and conduct a
set of experiments on Pre-trained Language Models (PLM) to establish strong
baselines for future research in this domain. The dataset is available at
https://blog.thedeep.io/humset/.Comment: Published at Findings of EMNLP 202
Simulation, construction and characterization of a piezoelectric transducer using rexolite as acoustic matching
This article describes the simulation and characterization of an ultrasonic transducer using a new material called Rexolite to be used as a matching element. This transducer was simulated using a commercial piezoelectric ceramic PIC255 at 8 MHz. Rexolite, the new material, presents an excellent acoustic matching, specially in terms of the acoustic impedance of water. Finite elements simulations were used in this work. Rexolite was considered as a suitable material in the construction of the transducer due to its malleability and acoustic properties, to validate the simulations a prototype transducer was constructed. Experimental measurements were used to determine the resonance frequency of the prototype transducer. Simulated and experimental results were very similar showing that Rexolite may be an excellent matching, particularly for medical applications
Proactive Highly Ambulatory Sensor Routing (PHASeR) protocol for mobile wireless sensor networks
This paper presents a novel multihop routing protocol for mobile wireless sensor networks called PHASeR (Proactive Highly Ambulatory Sensor Routing). The proposed protocol
uses a simple hop-count metric to enable the dynamic and robust routing of data towards the sink in mobile environments. It is motivated by the application of radiation mapping by unmanned vehicles, which requires the reliable and timely delivery of regular measurements to the sink. PHASeR maintains a gradient metric in mobile environments by using a global TDMA MAC layer. It also uses the technique of blind forwarding to pass messages through the network in a multipath manner. PHASeR is analysed mathematically based on packet delivery ratio, average packet delay, throughput and overhead. It is then simulated with varying mobility, scalability and traffic loads. The protocol gives good results over all measures, which suggests that it may also be suitable for a wider array of emerging applications
Cross-talk response analysis on a piezoelectric
The study of the cross-talk and its effects in the performance of a matrix array of piezoelectric elements is an important issue. This corresponds to the study of the cross mode of vibration of each one of the piezoelectric elements that form the ultrasonic array. The aim is to detect and measure the cross-talk that is generated for the cross mode of vibration. In order to accomplish this task, an array of 2x3 elements was designed and developed. This was constructed using 8 MHz piezoelectric ceramics. A number of configurations have been experimented, considering the excitation of an increasing number of elements, in order to detect and measure the propagation of wave interference. Initial results show the way cross-talk interferes the beam generated by the array, this causing attenuation of the main beam and other negative effects