ORCID Profile
0000-0002-1131-3382
Current Organisations
UNSW Sydney
,
Universidad Central de Chile
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Publisher: Springer Science and Business Media LLC
Date: 16-08-2022
DOI: 10.1007/S00521-022-07628-0
Abstract: Human interaction starts with a person approaching another one, respecting their personal space to prevent uncomfortable feelings. Spatial behavior, called proxemics, allows defining an acceptable distance so that the interaction process begins appropriately. In recent decades, human-agent interaction has been an area of interest for researchers, where it is proposed that artificial agents naturally interact with people. Thus, new alternatives are needed to allow optimal communication, avoiding humans feeling uncomfortable. Several works consider proxemic behavior with cognitive agents, where human-robot interaction techniques and machine learning are implemented. However, it is assumed that the personal space is fixed and known in advance, and the agent is only expected to make an optimal trajectory toward the person. In this work, we focus on studying the behavior of a reinforcement learning agent in a proxemic-based environment. Experiments were carried out implementing a grid-world problem and a continuous simulated robotic approaching environment. These environments assume that there is an issuer agent that provides non-conformity information. Our results suggest that the agent can identify regions where the issuer feels uncomfortable and find the best path to approach the issuer. The results obtained highlight the usefulness of reinforcement learning in order to identify proxemic regions.
Publisher: MDPI AG
Date: 30-06-2023
DOI: 10.3390/A16070326
Abstract: The opportunities for leveraging technology to enhance the efficiency of vessel port activities are vast. Applying video analytics to model and optimize certain processes offers a remarkable way to improve overall operations. Within the realm of vessel port activities, two crucial processes are vessel approximation and the docking process. This work specifically focuses on developing a vessel velocity estimation model and a docking mooring analytical system using a computer vision approach. The study introduces algorithms for speed estimation and mooring bitt detection, leveraging techniques such as the Structural Similarity Index (SSIM) for precise image comparison. The obtained results highlight the effectiveness of the proposed algorithms, demonstrating satisfactory speed estimation capabilities and successful identification of tied cables on the mooring bitts. These advancements pave the way for enhanced safety and efficiency in vessel docking procedures. However, further research and improvements are necessary to address challenges related to occlusions and illumination variations and explore additional techniques to enhance the models’ performance and applicability in real-world scenarios.
Publisher: IEEE
Date: 10-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: MDPI AG
Date: 16-12-2021
DOI: 10.3390/APP112411970
Abstract: Diabetes is a disease that occurs when the body presents an uncontrolled level of glucose that is capable of damaging the retina, leading to permanent damage of the eyes or vision loss. When diabetes affects the eyes, it is known as diabetic retinopathy, which became a global medical problem among elderly people. The fundus oculi technique involves observing the eyeball to diagnose or check the pathology evolution. In this work, we implement a convolutional neural network model to process a fundus oculi image to recognize the eyeball structure and determine the presence of diabetic retinopathy. The model’s parameters are optimized using the transfer-learning methodology for mapping an image with the corresponding label. The model training and testing are performed with a dataset of medical fundus oculi images and a pathology severity scale present in the eyeball as labels. The severity scale separates the images into five classes, from a healthy eyeball to a proliferative diabetic retinopathy presence. The latter is probably a blind patient. Our proposal presented an accuracy of 97.78%, allowing for the confident prediction of diabetic retinopathy in fundus oculi images.
Publisher: American Chemical Society (ACS)
Date: 21-12-2022
Publisher: IEEE
Date: 07-2018
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 07-2015
Publisher: Springer Science and Business Media LLC
Date: 18-09-2023
Publisher: ACM
Date: 07-01-2019
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Springer Science and Business Media LLC
Date: 28-08-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2016
Publisher: IEEE
Date: 11-2010
DOI: 10.1109/SCCC.2010.41
Publisher: IEEE
Date: 26-10-2020
Publisher: MDPI AG
Date: 03-2023
DOI: 10.3390/S23052681
Abstract: Deep Reinforcement Learning (DeepRL) methods have been widely used in robotics to learn about the environment and acquire behaviours autonomously. Deep Interactive Reinforcement 2 Learning (DeepIRL) includes interactive feedback from an external trainer or expert giving advice to help learners choose actions to speed up the learning process. However, current research has been limited to interactions that offer actionable advice to only the current state of the agent. Additionally, the information is discarded by the agent after a single use, which causes a duplicate process at the same state for a revisit. In this paper, we present Broad-Persistent Advising (BPA), an approach that retains and reuses the processed information. It not only helps trainers give more general advice relevant to similar states instead of only the current state, but also allows the agent to speed up the learning process. We tested the proposed approach in two continuous robotic scenarios, namely a cart pole balancing task and a simulated robot navigation task. The results demonstrated that the agent’s learning speed increased, as evidenced by the rising reward points of up to 37%, while maintaining the number of interactions required for the trainer, in comparison to the DeepIRL approach.
Publisher: IEEE
Date: 07-2020
Publisher: MDPI AG
Date: 19-09-2020
Abstract: Currently, unmanned aerial vehicles, such as drones, are becoming a part of our lives and extend to many areas of society, including the industrialized world. A common alternative for controlling the movements and actions of the drone is through unwired tactile interfaces, for which different remote control devices are used. However, control through such devices is not a natural, human-like communication interface, which sometimes is difficult to master for some users. In this research, we experimented with a domain-based speech recognition architecture to effectively control an unmanned aerial vehicle such as a drone. The drone control was performed in a more natural, human-like way to communicate the instructions. Moreover, we implemented an algorithm for command interpretation using both Spanish and English languages, as well as to control the movements of the drone in a simulated domestic environment. We conducted experiments involving participants giving voice commands to the drone in both languages in order to compare the effectiveness of each, considering the mother tongue of the participants in the experiment. Additionally, different levels of distortion were applied to the voice commands to test the proposed approach when it encountered noisy input signals. The results obtained showed that the unmanned aerial vehicle was capable of interpreting user voice instructions. Speech-to-action recognition improved for both languages with phoneme matching in comparison to only using the cloud-based algorithm without domain-based instructions. Using raw audio inputs, the cloud-based approach achieves 74.81% and 97.04% accuracy for English and Spanish instructions, respectively. However, with our phoneme matching approach the results are improved, yielding 93.33% accuracy for English and 100.00% accuracy for Spanish.
Publisher: IEEE
Date: 09-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2016
Publisher: MDPI AG
Date: 09-02-2021
DOI: 10.3390/BIOMIMETICS6010013
Abstract: Interactive reinforcement learning methods utilise an external information source to evaluate decisions and accelerate learning. Previous work has shown that human advice could significantly improve learning agents’ performance. When evaluating reinforcement learning algorithms, it is common to repeat experiments as parameters are altered or to gain a sufficient s le size. In this regard, to require human interaction every time an experiment is restarted is undesirable, particularly when the expense in doing so can be considerable. Additionally, reusing the same people for the experiment introduces bias, as they will learn the behaviour of the agent and the dynamics of the environment. This paper presents a methodology for evaluating interactive reinforcement learning agents by employing simulated users. Simulated users allow human knowledge, bias, and interaction to be simulated. The use of simulated users allows the development and testing of reinforcement learning agents, and can provide indicative results of agent performance under defined human constraints. While simulated users are no replacement for actual humans, they do offer an affordable and fast alternative for evaluative assisted agents. We introduce a method for performing a preliminary evaluation utilising simulated users to show how performance changes depending on the type of user assisting the agent. Moreover, we describe how human interaction may be simulated, and present an experiment illustrating the applicability of simulating users in evaluating agent performance when assisted by different types of trainers. Experimental results show that the use of this methodology allows for greater insight into the performance of interactive reinforcement learning agents when advised by different users. The use of simulated users with varying characteristics allows for evaluation of the impact of those characteristics on the behaviour of the learning agent.
Publisher: MDPI AG
Date: 09-03-2022
DOI: 10.3390/A15030091
Abstract: Currently, artificial intelligence is in an important period of growth. Due to the technology boom, it is now possible to solve problems that could not be resolved previously. For ex le, through goal-driven learning, it is possible that intelligent machines or agents may be able to perform tasks without human intervention. However, this also leads to the problem of understanding the agent’s decision making. Therefore, explainable goal-driven learning attempts to eliminate this gap. This work focuses on the adaptability of two explainability methods in continuous environments. The methods based on learning and introspection proposed a probability value for success to explain the agent’s behavior. These had already been tested in discrete environments. The continuous environment used in this study is the car-racing problem. This is a simulated car racing game that forms part of the Python Open AI Gym Library. The agents in this environment were trained with the Deep Q-Network algorithm, and in parallel the explainability methods were implemented. This research included a proposal for carrying out the adaptation and implementation of these methods in continuous states. The adaptation of the learning method produced major changes, implemented through an artificial neural network. The obtained probabilities of both methods were consistent throughout the experiments. The probability result was greater in the learning method. In terms of computational resources, the introspection method was slightly better than its counterpart.
Publisher: Springer Science and Business Media LLC
Date: 12-01-2023
DOI: 10.1007/S00521-021-06850-6
Abstract: Interactive reinforcement learning proposes the use of externally sourced information in order to speed up the learning process. When interacting with a learner agent, humans may provide either evaluative or informative advice. Prior research has focused on the effect of human-sourced advice by including real-time feedback on the interactive reinforcement learning process, specifically aiming to improve the learning speed of the agent, while minimising the time demands on the human. This work focuses on answering which of two approaches, evaluative or informative, is the preferred instructional approach for humans. Moreover, this work presents an experimental setup for a human trial designed to compare the methods people use to deliver advice in terms of human engagement. The results obtained show that users giving informative advice to the learner agents provide more accurate advice, are willing to assist the learner agent for a longer time, and provide more advice per episode. Additionally, self-evaluation from participants using the informative approach has indicated that the agent’s ability to follow the advice is higher, and therefore, they feel their own advice to be of higher accuracy when compared to people providing evaluative advice.
Publisher: Elsevier BV
Date: 10-2021
Publisher: IEEE
Date: 11-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: IEEE
Date: 11-2019
Publisher: Informa UK Limited
Date: 03-2018
Publisher: MDPI AG
Date: 19-10-2022
DOI: 10.3390/S22207963
Abstract: Computer vision techniques can monitor the rotational speed of rotating equipment or machines to understand their working conditions and prevent failures. Such techniques are highly precise, contactless, and potentially suitable for applications without massive setup changes. However, traditional vision sensors collect a significant amount of data to process and measure the rotation of high-speed systems, and they are susceptible to motion blur. This work proposes a new method for measuring rotational speed processing event-based data applied to high-speed systems using a neuromorphic sensor. This sensor produces event-based data and is designed to work with high temporal resolution and high dynamic range. The main advantages of the Event-based Angular Speed Measurement (EB-ASM) method are the high dynamic range, the absence of motion blurring, and the possibility of measuring multiple rotations simultaneously with a single device. The proposed method uses the time difference between spikes in a Kernel or Window selected in the sensor frame range. It is evaluated in two experimental scenarios by measuring a fan rotational speed and a Router Computer Numerical Control (CNC) spindle. The results compare measurements with a calibrated digital photo-tachometer. Based on the performed tests, the EB-ASM can measure the rotational speed with a mean absolute error of less than 0.2% for both scenarios.
Publisher: MDPI AG
Date: 12-08-2020
DOI: 10.3390/APP10165574
Abstract: Robots are extending their presence in domestic environments every day, it being more common to see them carrying out tasks in home scenarios. In the future, robots are expected to increasingly perform more complex tasks and, therefore, be able to acquire experience from different sources as quickly as possible. A plausible approach to address this issue is interactive feedback, where a trainer advises a learner on which actions should be taken from specific states to speed up the learning process. Moreover, deep reinforcement learning has been recently widely used in robotics to learn the environment and acquire new skills autonomously. However, an open issue when using deep reinforcement learning is the excessive time needed to learn a task from raw input images. In this work, we propose a deep reinforcement learning approach with interactive feedback to learn a domestic task in a Human–Robot scenario. We compare three different learning methods using a simulated robotic arm for the task of organizing different objects the proposed methods are (i) deep reinforcement learning (DeepRL) (ii) interactive deep reinforcement learning using a previously trained artificial agent as an advisor (agent–IDeepRL) and (iii) interactive deep reinforcement learning using a human advisor (human–IDeepRL). We demonstrate that interactive approaches provide advantages for the learning process. The obtained results show that a learner agent, using either agent–IDeepRL or human–IDeepRL, completes the given task earlier and has fewer mistakes compared to the autonomous DeepRL approach.
Publisher: IEEE
Date: 10-2014
Publisher: IEEE
Date: 26-10-2020
Publisher: Springer Science and Business Media LLC
Date: 04-09-2023
Publisher: ACM
Date: 10-11-2020
No related grants have been discovered for Francisco Cruz.