scholarly journals Attainment Regions in Feature-Parameter Space for High-Level Debugging in Autonomous Robots

Author(s):  
Simon C. Smith ◽  
Subramanian Ramamoorthy
2016 ◽  
Vol 1 (1) ◽  
pp. 469-476 ◽  
Author(s):  
Hamal Marino ◽  
Mirko Ferrati ◽  
Alessandro Settimi ◽  
Carlos Rosales ◽  
Marco Gabiccini

2020 ◽  
Vol 63 (1) ◽  
pp. 165-183
Author(s):  
Paweł Polak ◽  
Roman Krzanowski

Abstract Social robotics are autonomous robots or Artificial Moral Agents (AMA), that will interact respect and embody human ethical values. However, the conceptual and practical problems of building such systems have not yet been resolved, playing a role of significant challenge for computational modeling. It seems that the lack of success in constructing robots, ceteris paribus, is due to the conceptual and algorithmic limitations of the current design of ethical robots. This paper proposes a new approach for developing ethical capacities in robotic systems, one based on the concept of Aristotelian phronesis. Phronesis in principle reflexes closer human ethics than the ethical paradigms we employ today in ethical robotics. This paper describes the essential features of phronesis and proposes a high-level architecture for implementing phronetic principles in autonomous robots. Phronetic robotics is in its early stages of conceptualization, so many of the presented ideas are speculative and require further research.2


2020 ◽  
Author(s):  
Jorge Zazueta ◽  
Elvio Accinelli

We develop a simple mathematical model describing the dynamics of the gender gap in a labor market niche and study the effects of bias, market size, and market dynamism in the evolution of the system. A high-level characterization of the system is suggested by studying a large sample of the parameter space and specific cases of interest to policymaking are explored.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Joanne Pransky

Purpose The purpose of this paper is to provide a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry engineer-turned entrepreneur regarding his pioneering efforts in starting robotic companies and commercializing technological inventions. The paper aims to discuss these issues. Design/methodology/approach The interviewee is Brennard Pierce, a world-class robotics designer and serial entrepreneur. Pierce is currently consulting in robotics after exiting from his latest startup as cofounder and chief robotics officer of Bear Robotics. Pierce discusses what led him to the field of robotics, the success of Bear Robotics, the challenges he’s faced and his future goals. Findings Pierce received a Bachelor of Science in computer science from Exeter University. He then founded his first startup, 5TWO, a custom software company. Always passionate about robotics as a hobby and now wanting to pursue the field professionally, he sold 5TWO to obtain a Master of Science, Robotics degree from the newly formed Bristol Robotics Lab (BRL) at Bristol University. After BRL, where he designed and built a biped robot that learned to walk using evolutionary algorithms, he joined the Robotics Research team at Carnegie Mellon University where he worked on a full-size humanoid robot for a large electronics company, designing and executing simple experiments for balancing. He then spent the next six years as a PhD candidate and robotics researcher at the Technical University Munich (TUM), Institute for Cognitive Science, where he built a compliant humanoid robot and a new generation of field programmable gate array-based robotic controllers. Afterwards, Pierce established the robotic startup Robotise in Munich to commercialize the omni-directional mobile platforms that he had developed at TUM. A couple of years later, Pierce left Robotise to cofound Bear Robotics, a Silicon Valley based company that brings autonomous robots to the restaurant industry. He remained at Bear Robotics for four years as chief robotics officer. He is presently a robotics consultant, waiting for post-COVID before beginning his next robotic startup. Originality/value Pierce is a seasoned roboticist and a successful entrepreneur. He has 15+ years’ of unique experience in both designing robotic hardware and writing low level embedded and high level cloud software. During his career he has founded three companies, managed small to middle sized interdisciplinary teams, and hired approximately 100 employees of all levels. Pierce’s robotic startup in Munich, Robotise, was solely based on his idea, design and implementation for an autonomous mobile delivery system. The third company he cofounded, Bear Robotics, successfully raised a $32m Series A funding lead by SoftBank. Bear Robotics is the recipient of the USA’s National Restaurant Association Kitchen Innovation Award; Fast Company’s World Changing Ideas Awards; and the Hospitality Innovation Planet 2020 Award.


Author(s):  
Adam Csapo ◽  
◽  
Péter Baranyi ◽  

Cognitive Infocommunications (CogInfoCom) messages that are used to carry information on the state of the same high-level concept can be regarded as belonging to a CogInfoCom channel. Such channels can be generated using any kind of parametric model. By changing the values of the parameters, it is possible to arrive at a large variety of CogInfoCom messages, a subset of which can belong to a CogInfoCom channel – provided they are perceptually well-suited to the purpose of conveying information on the same highlevel concept. Thus, for any CogInfoCom channel, we may speak of a parameter space and a perceptual space that is created by the totality of messages in the CogInfoCom channel. In this paper, we argue that in general, the relationship between the parameter space and the perceptual space is highly non-linear. For this reason, it is extremely difficult for the designer of a CogInfoCom channel to tune the parameters in such a way that the resulting CogInfoCom messages are perceptually continuous, and suitable to carry information on a single high-level concept. To address this problem, we propose a cognitive artifact that uses a rank concept available in tensor algebra to provide the designer of CogInfoCom channels with practical tradeoffs between complexity and interpretability. We refer to the artifact as the Spiral Discovery Method (SDM).


2019 ◽  
Vol 1 (12) ◽  
Author(s):  
Deon de Jager ◽  
Yahya Zweiri ◽  
Dimitrios Makris

AbstractHigh-level, real-time mission control of semi-autonomous robots, deployed in remote and dynamic environments, remains a challenge. Control models, learnt from a knowledgebase, quickly become obsolete when the environment or the knowledgebase changes. This research study introduces a cognitive reasoning process, to select the optimal action, using the most relevant knowledge from the knowledgebase, subject to observed evidence. The approach in this study introduces an adaptive entropy-based set-based particle swarm algorithm (AE-SPSO) and a novel, adaptive entropy-based fitness quantification (AEFQ) algorithm for evidence-based optimization of the knowledge. The performance of the AE-SPSO and AEFQ algorithms are experimentally evaluated with two unmanned aerial vehicle (UAV) benchmark missions: (1) relocating the UAV to a charging station and (2) collecting and delivering a package. Performance is measured by inspecting the success and completeness of the mission and the accuracy of autonomous flight control. The results show that the AE-SPSO/AEFQ approach successfully finds the optimal state-transition for each mission task and that autonomous flight control is successfully achieved.


2019 ◽  
Vol 79 (11) ◽  
Author(s):  
Essodjolo Kpatcha ◽  
Iñaki Lara ◽  
Daniel E. López-Fogliani ◽  
Carlos Muñoz ◽  
Natsumi Nagata ◽  
...  

AbstractWithin the framework of the $$\mu \nu \mathrm{SSM}$$μνSSM, a displaced dilepton signal is expected at the LHC from the decay of a tau left sneutrino as the lightest supersymmetric particle (LSP) with a mass in the range 45–100 GeV. We compare the predictions of this scenario with the ATLAS search for long-lived particles using displaced lepton pairs in pp collisions, considering an optimization of the trigger requirements by means of a high level trigger that exploits tracker information. The analysis is carried out in the general case of three families of right-handed neutrino superfields, where all the neutrinos get contributions to their masses at tree level. To analyze the parameter space, we sample the $$\mu \nu $$μνSSM for a tau left sneutrino LSP with proper decay length $$c\tau > 0.1 \, \hbox {mm}$$cτ>0.1mm using a likelihood data-driven method, and paying special attention to reproduce the current experimental data on neutrino and Higgs physics, as well as flavor observables. The sneutrino is special in the $$\mu \nu \mathrm{SSM}$$μνSSM since its couplings have to be chosen so that the neutrino oscillation data are reproduced. We find that important regions of the parameter space can be probed at the LHC run 3.


2021 ◽  
Author(s):  
Georgios Adosoglou ◽  
Seonho Park ◽  
Yiannis Ampatzidis ◽  
Panos M. Pardalosa

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