Pub Date : 2023-01-01DOI: 10.1016/J.IFACOL.2020.12.1106
T. Ahmed-Ali, F. Lamnabhi-Lagarrigue, H. Khalil
{"title":"High-gain observer-based output feedback control with sensor dynamic governed by parabolic PDE","authors":"T. Ahmed-Ali, F. Lamnabhi-Lagarrigue, H. Khalil","doi":"10.1016/J.IFACOL.2020.12.1106","DOIUrl":"https://doi.org/10.1016/J.IFACOL.2020.12.1106","url":null,"abstract":"","PeriodicalId":13196,"journal":{"name":"IEEE Robotics Autom. Mag.","volume":"34 1","pages":"110664"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90990474","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}
Pub Date : 2022-12-14DOI: 10.48550/arXiv.2212.07534
Yongqiang Wang, T. Başar
Privacy protection and nonconvexity are two challenging problems in decentralized optimization and learning involving sensitive data. Despite some recent advances addressing each of the two problems separately, no results have been reported that have theoretical guarantees on both privacy protection and saddle/maximum avoidance in decentralized nonconvex optimization. We propose a new algorithm for decentralized nonconvex optimization that can enable both rigorous differential privacy and saddle/maximum avoiding performance. The new algorithm allows the incorporation of persistent additive noise to enable rigorous differential privacy for data samples, gradients, and intermediate optimization variables without losing provable convergence, and thus circumventing the dilemma of trading accuracy for privacy in differential privacy design. More interestingly, the algorithm is theoretically proven to be able to efficiently { guarantee accuracy by avoiding} convergence to local maxima and saddle points, which has not been reported before in the literature on decentralized nonconvex optimization. The algorithm is efficient in both communication (it only shares one variable in each iteration) and computation (it is encryption-free), and hence is promising for large-scale nonconvex optimization and learning involving high-dimensional optimization parameters. Numerical experiments for both a decentralized estimation problem and an Independent Component Analysis (ICA) problem confirm the effectiveness of the proposed approach.
{"title":"Decentralized Nonconvex Optimization with Guaranteed Privacy and Accuracy","authors":"Yongqiang Wang, T. Başar","doi":"10.48550/arXiv.2212.07534","DOIUrl":"https://doi.org/10.48550/arXiv.2212.07534","url":null,"abstract":"Privacy protection and nonconvexity are two challenging problems in decentralized optimization and learning involving sensitive data. Despite some recent advances addressing each of the two problems separately, no results have been reported that have theoretical guarantees on both privacy protection and saddle/maximum avoidance in decentralized nonconvex optimization. We propose a new algorithm for decentralized nonconvex optimization that can enable both rigorous differential privacy and saddle/maximum avoiding performance. The new algorithm allows the incorporation of persistent additive noise to enable rigorous differential privacy for data samples, gradients, and intermediate optimization variables without losing provable convergence, and thus circumventing the dilemma of trading accuracy for privacy in differential privacy design. More interestingly, the algorithm is theoretically proven to be able to efficiently { guarantee accuracy by avoiding} convergence to local maxima and saddle points, which has not been reported before in the literature on decentralized nonconvex optimization. The algorithm is efficient in both communication (it only shares one variable in each iteration) and computation (it is encryption-free), and hence is promising for large-scale nonconvex optimization and learning involving high-dimensional optimization parameters. Numerical experiments for both a decentralized estimation problem and an Independent Component Analysis (ICA) problem confirm the effectiveness of the proposed approach.","PeriodicalId":13196,"journal":{"name":"IEEE Robotics Autom. Mag.","volume":"37 1","pages":"110858"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83143416","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}
Pub Date : 2022-12-10DOI: 10.48550/arXiv.2212.05364
Yuanzhe Xuan, Yongqiang Wang
Privacy protection has become an increasingly pressing requirement in distributed optimization. However, equipping distributed optimization with differential privacy, the state-of-the-art privacy protection mechanism, will unavoidably compromise optimization accuracy. In this paper, we propose an algorithm to achieve rigorous $epsilon$-differential privacy in gradient-tracking based distributed optimization with enhanced optimization accuracy. More specifically, to suppress the influence of differential-privacy noise, we propose a new robust gradient-tracking based distributed optimization algorithm that allows both stepsize and the variance of injected noise to vary with time. Then, we establish a new analyzing approach that can characterize the convergence of the gradient-tracking based algorithm under both constant and time-varying stespsizes. To our knowledge, this is the first analyzing framework that can treat gradient-tracking based distributed optimization under both constant and time-varying stepsizes in a unified manner. More importantly, the new analyzing approach gives a much less conservative analytical bound on the stepsize compared with existing proof techniques for gradient-tracking based distributed optimization. We also theoretically characterize the influence of differential-privacy design on the accuracy of distributed optimization, which reveals that inter-agent interaction has a significant impact on the final optimization accuracy. Numerical simulation results confirm the theoretical predictions.
{"title":"Gradient-tracking Based Differentially Private Distributed Optimization with Enhanced Optimization Accuracy","authors":"Yuanzhe Xuan, Yongqiang Wang","doi":"10.48550/arXiv.2212.05364","DOIUrl":"https://doi.org/10.48550/arXiv.2212.05364","url":null,"abstract":"Privacy protection has become an increasingly pressing requirement in distributed optimization. However, equipping distributed optimization with differential privacy, the state-of-the-art privacy protection mechanism, will unavoidably compromise optimization accuracy. In this paper, we propose an algorithm to achieve rigorous $epsilon$-differential privacy in gradient-tracking based distributed optimization with enhanced optimization accuracy. More specifically, to suppress the influence of differential-privacy noise, we propose a new robust gradient-tracking based distributed optimization algorithm that allows both stepsize and the variance of injected noise to vary with time. Then, we establish a new analyzing approach that can characterize the convergence of the gradient-tracking based algorithm under both constant and time-varying stespsizes. To our knowledge, this is the first analyzing framework that can treat gradient-tracking based distributed optimization under both constant and time-varying stepsizes in a unified manner. More importantly, the new analyzing approach gives a much less conservative analytical bound on the stepsize compared with existing proof techniques for gradient-tracking based distributed optimization. We also theoretically characterize the influence of differential-privacy design on the accuracy of distributed optimization, which reveals that inter-agent interaction has a significant impact on the final optimization accuracy. Numerical simulation results confirm the theoretical predictions.","PeriodicalId":13196,"journal":{"name":"IEEE Robotics Autom. Mag.","volume":"39 1","pages":"111150"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76738790","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}
Pub Date : 2022-12-01DOI: 10.1109/mra.2022.3213199
Chenguang Yang, Shan Luo, N. Lepora, F. Ficuciello, Dongheui Lee, Weiwei Wan, C. Su
{"title":"Biomimetic Perception, Cognition, and Control: From Nature to Robots [From the Guest Editors]","authors":"Chenguang Yang, Shan Luo, N. Lepora, F. Ficuciello, Dongheui Lee, Weiwei Wan, C. Su","doi":"10.1109/mra.2022.3213199","DOIUrl":"https://doi.org/10.1109/mra.2022.3213199","url":null,"abstract":"","PeriodicalId":13196,"journal":{"name":"IEEE Robotics Autom. Mag.","volume":"64 1","pages":"8"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81729587","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}
Pub Date : 2022-12-01DOI: 10.1109/mra.2022.3213466
Xuesu Xiao, Zifan Xu, Zizhao Wang, Yunlong Song, Garrett Warnell, P. Stone, Tingnan Zhang, Shravan Ravi, Gary Wang, Haresh Karnan, Joydeep Biswas, Nicholas Mohammad, Lauren Bramblett, Rahul Peddi, N. Bezzo, Zhanteng Xie, P. Dames
148 • IEEE ROBOTICS & AUTOMATION MAGAZINE • DECEMBER 2022 T he Benchmark Autonomous Robot Navigation (BARN) Challenge took place at the 2022 IEEE International Conference on Robotics and Automation (ICRA), in Philadelphia, PA, USA. The aim of the challenge was to evaluate state-ofthe-art autonomous ground navigation systems for moving robots through highly constrained environments in a safe and efficient manner. Specifically, the task was to navigate a standardized differential drive ground robot from a predefined start location to a goal location as quickly as possible without colliding with any obstacles, both in simulation and in the real world. Five teams from all over the world participated in the qualifying simu lation competition, three of which were invited to compete with one another at a set of physical obstacle courses at the conference center in Philadelphia. The competition results suggest that autonomous ground navigation in highly con strained spaces, despite seeming simple for experienced ro boticists, is actually far from being a solved problem. In this article, we discuss the challenge, the ap proaches used by the top three winning teams, and lessons learned to direct future research.
{"title":"Autonomous Ground Navigation in Highly Constrained Spaces: Lessons Learned From the Benchmark Autonomous Robot Navigation Challenge at ICRA 2022 [Competitions]","authors":"Xuesu Xiao, Zifan Xu, Zizhao Wang, Yunlong Song, Garrett Warnell, P. Stone, Tingnan Zhang, Shravan Ravi, Gary Wang, Haresh Karnan, Joydeep Biswas, Nicholas Mohammad, Lauren Bramblett, Rahul Peddi, N. Bezzo, Zhanteng Xie, P. Dames","doi":"10.1109/mra.2022.3213466","DOIUrl":"https://doi.org/10.1109/mra.2022.3213466","url":null,"abstract":"148 • IEEE ROBOTICS & AUTOMATION MAGAZINE • DECEMBER 2022 T he Benchmark Autonomous Robot Navigation (BARN) Challenge took place at the 2022 IEEE International Conference on Robotics and Automation (ICRA), in Philadelphia, PA, USA. The aim of the challenge was to evaluate state-ofthe-art autonomous ground navigation systems for moving robots through highly constrained environments in a safe and efficient manner. Specifically, the task was to navigate a standardized differential drive ground robot from a predefined start location to a goal location as quickly as possible without colliding with any obstacles, both in simulation and in the real world. Five teams from all over the world participated in the qualifying simu lation competition, three of which were invited to compete with one another at a set of physical obstacle courses at the conference center in Philadelphia. The competition results suggest that autonomous ground navigation in highly con strained spaces, despite seeming simple for experienced ro boticists, is actually far from being a solved problem. In this article, we discuss the challenge, the ap proaches used by the top three winning teams, and lessons learned to direct future research.","PeriodicalId":13196,"journal":{"name":"IEEE Robotics Autom. Mag.","volume":"99 2 1","pages":"148-156"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90990537","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}
Pub Date : 2022-12-01DOI: 10.1109/mra.2022.3213136
E. Henten, A. Tabb, J. Billingsley, Marija Popovic, M. Deng, J. F. Reid
{"title":"Agricultural Robotics and Automation [TC Spotlight]","authors":"E. Henten, A. Tabb, J. Billingsley, Marija Popovic, M. Deng, J. F. Reid","doi":"10.1109/mra.2022.3213136","DOIUrl":"https://doi.org/10.1109/mra.2022.3213136","url":null,"abstract":"","PeriodicalId":13196,"journal":{"name":"IEEE Robotics Autom. Mag.","volume":"32 1","pages":"145-147"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85839470","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}
Pub Date : 2022-12-01DOI: 10.1109/mra.2022.3213205
F. Stulp, Michael Spranger, Kim D. Listmann, S. Doncieux, Moritz Tenorth, G. Konidaris, P. Abbeel
{"title":"Innovation Paths for Machine Learning in Robotics [Industry Activities]","authors":"F. Stulp, Michael Spranger, Kim D. Listmann, S. Doncieux, Moritz Tenorth, G. Konidaris, P. Abbeel","doi":"10.1109/mra.2022.3213205","DOIUrl":"https://doi.org/10.1109/mra.2022.3213205","url":null,"abstract":"","PeriodicalId":13196,"journal":{"name":"IEEE Robotics Autom. Mag.","volume":"9 1","pages":"141-144"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87382852","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}
Pub Date : 2022-12-01DOI: 10.1109/mra.2022.3213467
Ruxandra Lupu, M. Caccia, E. Zereik, Rosangela Barcaro
{"title":"Women in Blue: Toward a Better Understanding of the Gender Gap in Marine Robotics [Women in Engineering]","authors":"Ruxandra Lupu, M. Caccia, E. Zereik, Rosangela Barcaro","doi":"10.1109/mra.2022.3213467","DOIUrl":"https://doi.org/10.1109/mra.2022.3213467","url":null,"abstract":"","PeriodicalId":13196,"journal":{"name":"IEEE Robotics Autom. Mag.","volume":"101 1","pages":"138-140"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80813748","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}
Pub Date : 2022-12-01DOI: 10.1109/mra.2022.3213198
Yi Guo
The COVID-19 pandemic has changed a lot of things, one of which is human behavior. For me, I found a new hobby of hiking during the first year of the pandemic. I hiked in dozens of state parks around me during the fall and winter seasons, some of which I did not even know existed before the pandemic. I felt relieved both physically and mentally after the weekend hiking trips, and it was helpful for me to reduce the Zoom fatigue built up during work days.
{"title":"Humans and the Environment [From the Editor's Desk]","authors":"Yi Guo","doi":"10.1109/mra.2022.3213198","DOIUrl":"https://doi.org/10.1109/mra.2022.3213198","url":null,"abstract":"The COVID-19 pandemic has changed a lot of things, one of which is human behavior. For me, I found a new hobby of hiking during the first year of the pandemic. I hiked in dozens of state parks around me during the fall and winter seasons, some of which I did not even know existed before the pandemic. I felt relieved both physically and mentally after the weekend hiking trips, and it was helpful for me to reduce the Zoom fatigue built up during work days.","PeriodicalId":13196,"journal":{"name":"IEEE Robotics Autom. Mag.","volume":"23 1","pages":"4"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83465844","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}
Pub Date : 2022-12-01DOI: 10.1109/mra.2022.3214390
F. Park
{"title":"Rethinking the Research Paper [President's Message]","authors":"F. Park","doi":"10.1109/mra.2022.3214390","DOIUrl":"https://doi.org/10.1109/mra.2022.3214390","url":null,"abstract":"","PeriodicalId":13196,"journal":{"name":"IEEE Robotics Autom. Mag.","volume":"1 1","pages":"6"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78007935","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}
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