B. Faria, Daniel Dias, Luis Paulo Reis, A. Moreira
{"title":"Multimodal Interaction and Serious Game for Assistive Robotic Devices in a Simulated Environment","authors":"B. Faria, Daniel Dias, Luis Paulo Reis, A. Moreira","doi":"10.1109/ICARSC.2016.54","DOIUrl":"https://doi.org/10.1109/ICARSC.2016.54","url":null,"abstract":"Sports and physical activities allow people with disabilities to have better quality of life. The proposed work aimed to develop a multimodal interaction platform of robotic devices in a simulated environment for users to train different interface options. The suggested scenarios allow a user to interact with an Intelligent Wheelchair (IW) and with an Intelligent Robotic Ramp (IRR) performing different tasks individually or with a multiplayer option. The main objective of this multimodal interaction platform is to allow users, with severe disabilities, to move around and inclusive to play the Boccia Game more independently and autonomously. A preliminary set of experiments with 27 volunteers tested the scenarios and the multimodal interface for driving the intelligent wheelchair and to maneuver the IRR. The results show excellent performance when users maneuver the IRR in which the success achieved 90%. All dimensions of CEGEQ questionnaire presented good results. Therefore the solution created is quite satisfactory for a user point of view.","PeriodicalId":260641,"journal":{"name":"2016 International Conference on Autonomous Robot Systems and Competitions (ICARSC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133670587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"User Centered Design of an Autonomous Mobile Robot Vending Machine Concept","authors":"Andre S. P. H. Navarro, C. Cardeira, C. Monteiro","doi":"10.1109/ICARSC.2016.67","DOIUrl":"https://doi.org/10.1109/ICARSC.2016.67","url":null,"abstract":"In this paper a new Autonomous Mobile Robot Vending Machine concept is developed for food and beverages distribution in beaches. The concept was developed and submitted to end users appraisal, in order to capture their perceptions and preferences, and the resulting knowledge guides future developments towards the real implementation of the robot. This methodology, which uses multivariate statistics, is important to bring the product closer to the market. Nowadays this analysis becomes particularly relevant since it increases the Technology Readiness Levels of a new product far beyond those achieved with a technical prototype.","PeriodicalId":260641,"journal":{"name":"2016 International Conference on Autonomous Robot Systems and Competitions (ICARSC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123941379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Helder Ribeiro, Pedro Silva, Ricardo Roriz, Tiago Maia, Rui Saraiva, G. Lopes, A. Ribeiro
{"title":"Fast Computational Processing for Mobile Robots' Self-Localization","authors":"Helder Ribeiro, Pedro Silva, Ricardo Roriz, Tiago Maia, Rui Saraiva, G. Lopes, A. Ribeiro","doi":"10.1109/ICARSC.2016.40","DOIUrl":"https://doi.org/10.1109/ICARSC.2016.40","url":null,"abstract":"This paper intends to present a different approach to solve the Self-Localization problem regarding a RoboCup's Middle Size League game, developed by MINHO team researchers. The method uses white field markings as key points, to compute the position with least error, creating an error-based graphic where the minimum corresponds to the real position, that are computed by comparing the key (line) points with a precomputed set of values for each position. This approach allows a very fast local and global localization calculation, allowing the global localization to be used more often, while driving the estimate to its real value. Differently from the majority of other teams in this league, it was important to come up with a new and improved method to solve the traditional slow Self-Localization problem.","PeriodicalId":260641,"journal":{"name":"2016 International Conference on Autonomous Robot Systems and Competitions (ICARSC)","volume":"256 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122655836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Abdolmaleki, D. Simões, N. Lau, Luis Paulo Reis, G. Neumann
{"title":"Contextual Relative Entropy Policy Search with Covariance Matrix Adaptation","authors":"A. Abdolmaleki, D. Simões, N. Lau, Luis Paulo Reis, G. Neumann","doi":"10.1109/ICARSC.2016.31","DOIUrl":"https://doi.org/10.1109/ICARSC.2016.31","url":null,"abstract":"Stochastic search algorithms are black-box optimizers of an objective function. They have recently gained a lot of attention in operations research, machine learning and policy search of robot motor skills due to their ease of use and their generality. However, with slightly different tasks or objective functions, many stochastic search algorithms require complete re-learning in order to adapt the solution to the new objective function or the new context. As such, we consider the contextual stochastic search paradigm. Here, we want to find good parameter vectors for multiple related tasks, where each task is described by a continuous context vector. Hence, the objective function might change slightly for each parameter vector evaluation. Contextual algorithms have been investigated in the field of policy search. However, contextual policy search algorithms typically suffer from premature convergence and perform unfavourably in comparison with state of the art stochastic search methods. In this paper, we investigate a contextual stochastic search algorithm known as Contextual Relative Entropy Policy Search (CREPS), an informationtheoretic algorithm that can learn for multiple tasks simultaneously. We extend that algorithm with a covariance matrix adaptation technique that alleviates the premature convergence problem. We call the new algorithm Contextual Relative Entropy Policy Search with Covariance Matrix Adaptation (CREPS-CMA). We will show that CREPS-CMA outperforms the original CREPS by orders of magnitude. We illustrate the performance of CREPS-CMA on several contextual tasks, including a complex simulated robot kick task.","PeriodicalId":260641,"journal":{"name":"2016 International Conference on Autonomous Robot Systems and Competitions (ICARSC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128516857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}