1,720,967 research outputs found
Efficient Exploration of Body Surface with Tactile Sensors on Humanoid Robots
Full text linkZatímco u člověka hraje hmat velmi důležitou roli, v robotice je mu věnována malá pozornost. Dotyková zpětná vazba má přitom obrovský potenciál, od automatické kalibrace robotu po bezpečnou interakci člověka s robotem. V oblasti aktivního učení a vnitřní motivace existuje celá řada prací, ale málokdy jsou algoritmy testovány na robotech s dotykovou zpětnou vazbou. Pokud je taktilní modalita přece jen použita, jedná se často o velmi jednoduché simulace např. planárních manipulátorů. V této práci jsem k existujícímu simulátoru robota iCub, který se používá v kognitivní vývojové robotice, v Gazebo přidal taktilní zpětnou vazbu. Simulátor jsem použil k sérii experimentů zaměřených na aktivní průzkum povrchu těla. K tomu jsem použil algoritmy založené na “goal babbling” a “exploration by disagreement”. Také jsem porovnal různé způsoby implementace inverzních modelů na úloze taktilní explorace. Po integraci simulátoru kůže do oficiálního iCub Gazebo simulátoru bude tento nástroj k dispozici široké komunitě uživatelů.Sense of touch plays an important role in the life of a person, however it is underutilised and understudied in the field of robotics. Tactile sensory modality has large potential in many areas, from robots building and calibrating models of their bodies using tactile feedback, to enabling safe human-robot interaction. Although rich literature exists on the topics of active learning and intrinsic motivation, authors rarely test their hypotheses on robots with the sense of touch. And when they do, they often prefer to use extremely simple simulations of planar manipulators. In this thesis, I address this problem by developing an artificial skin simulator for the iCub humanoid robot used for research in cognitive developmental robotics, and using it for experiments in efficient exploration of the robot’s body. I have successfully implemented the artificial skin simulator for the iCub humanoid robot and used it to perform a set of experiments in body surface exploration. I have applied the goal babbling exploration framework and exploration by disagreement algorithm to efficiently explore the simulated robot’s body surface. I have also compared several inverse body models suitable for the task of tactile exploration. Once the artificial skin simulator is accepted into the iCub codebase, it will make it easier for other researchers to perform experiments involving the sense of touch on a humanoid robot
Work performance evaluation of heavy-duty mobile machines (HDMMs)
Full text linkThe construction industry is crucial for economic growth, but its productivity has not improved much despite its importance. Heavy-duty mobile machines (HDMMs), particularly excavators, play a central role in construction projects, with their productivity directly impacting projects\u27 productivity and costs. This dissertation aims to tackle several challenges regarding the automatic productivity estimation of an excavator in earth-moving operations, such as loading, trenching, and grading.
In the beginning, the significance of the construction industry and the critical role of HDMMs within it are discussed. It highlights the challenges faced by the industry, including low productivity growth and outdated practices, emphasizing the need for automated productivity estimation and progress monitoring. Then, an excavator is introduced as the main application in the research study. In the next phase, existing research studies for the productivity estimation of HDMMs are thoroughly explored to identify research gaps and to design multiple research questions that drive the dissertation\u27s focus.
Capturing motion information using inertial measurement units (IMUs) holds promise for recognizing activities and automatically estimating cycle time and productivity. Also, the importance of analysis of working conditions and estimating theoretical cycle time and productivity is stated. In addition, 3D sensors and building information modeling (BIM) can be integrated to enhance the productivity estimation and progress monitoring of an excavator in quality-centered tasks, such as grading and trenching operations.
First, an activity recognition method is proposed to identify the excavator working cycle using supervised classification methods and motion information, such as angular velocities and joint angles, obtained from four IMUs attached to moving parts of an excavator, including the swing body, boom, arm, and bucket. Human operators perform tasks using a medium-rated excavator under different working conditions, such as different types of material, swing angle, digging depth, and weather conditions to collect a dataset. The proposed method can effectively recognize the working cycles of an excavator. Task recognition can aid management teams in monitoring productivity and progress, optimizing resource allocation, and scheduling. Using the results of the task recognition algorithm, productivity can be calculated based on task-specific metrics.
Next, an approach is designed to automatically determine the productivity and operational effectiveness of an excavator in the loading operation. Firstly, an algorithm is proposed to recognize the excavator\u27s sub-tasks using supervised learning and motion data obtained from IMUs. Then, a method is presented to estimate the actual cycle time based on the sequence of activities detected using the trained classification model. The actual cycle time cannot solely reveal the machine\u27s performance since operating conditions can significantly influence the cycle time. Therefore, a reference is required to analyze the actual cycle time. Secondly, the theoretical cycle time of an excavator is automatically estimated based on the operating conditions, such as swing angle and digging depth. Thirdly, the relative cycle time is obtained by dividing the theoretical cycle time by the actual cycle time. The relative cycle time index can effectively monitor the performance of an excavator in loading operations and can be useful for worksite managers to monitor the performance of each machine in worksites.
In the next step, a technique is proposed to estimate the excavator’s actual productivity in trenching and grading operations. In these tasks, the quantity of material moved is not significant; precision within specified tolerances is the key focus. The productivity definitions for trenching and grading operations are the trench\u27s length per unit of time and graded area per unit of time, respectively. In the method, a height map from working areas is constructed. Also, BIM is utilized to acquire information regarding the target model and required accuracy. The productivity is estimated using the map comparison between the working areas and the desired model. The method can effectively estimate productivity and monitor the progress of these operations. The obtained information can guide managers to track the productivity of each individual machine and modify planning and time-scheduling.
This dissertation employs advanced technologies, such as IMUs, machine learning techniques, elevation terrain mapping algorithms, and BIM. It aims to streamline productivity estimation and progress monitoring for excavators, ultimately contributing to more efficient and successful construction projects. It underscores the potential for future research to enhance these methodologies, expand their applicability to other HDMMs and tasks, and address remaining challenges to propel the construction industry towards greater productivity and sustainability
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Interactive imitation learning for dexterous robotic manipulation: challenges and perspectives—a survey
No full textDexterous manipulation is a crucial yet highly complex challenge in humanoid robotics, demanding precise, adaptable, and sample-efficient learning methods. As humanoid robots are usually designed to operate in human-centric environments and interact with everyday objects, mastering dexterous manipulation is critical for real-world deployment. Traditional approaches, such as reinforcement learning and imitation learning, have made significant strides, but they often struggle due to the unique challenges of real-world dexterous manipulation, including high-dimensional control, limited training data, and covariate shift. This survey provides a comprehensive overview of these challenges and reviews existing learning-based methods for real-world dexterous manipulation, spanning imitation learning, reinforcement learning, and hybrid approaches. A promising yet underexplored direction is interactive imitation learning, where human feedback actively refines a robot’s behavior during training. While interactive imitation learning has shown success in various robotic tasks, its application to dexterous manipulation remains limited. To address this gap, we examine current interactive imitation learning techniques applied to other robotic tasks and discuss how these methods can be adapted to enhance dexterous manipulation. By synthesizing state-of-the-art research, this paper highlights key challenges, identifies gaps in current methodologies, and outlines potential directions for leveraging interactive imitation learning to improve dexterous robotic skills
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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