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\n \n\n \n \n Kontoudis, G. P; and Stilwell, D.\n\n\n \n \n \n \n \n Decentralized Federated Learning using Gaussian Processes.\n \n \n \n \n\n\n \n\n\n\n In IEEE International Symposium on Multi-Robot and Multi-Agent Systems (MRS), December 2023. \n \n\n\n\n
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@inproceedings{kontoudis2023MRS,\n  title={Decentralized Federated Learning using Gaussian Processes},\n  author={Kontoudis, George P and Stilwell, Daniel},\n  booktitle={IEEE International Symposium on Multi-Robot and Multi-Agent Systems (MRS)},\n  abstract={Gaussian process (GP) training of kernel hyperparameters still remains a major challenge due to high computational complexity. The typical GP training method employs maximum likelihood estimation to solve an optimization problem that requires cubic computations for each iteration. In addition, GP training in multi-agent systems requires significant amount of inter-agent communication that typically involves sharing of local data. In this paper, we propose a scalable optimization algorithm for decentralized learning of GP hyperparameters in multi-agent systems. To distribute the implementation of GP training, we employ the alternating direction method of multipliers (ADMM). We provide a closed-form solution of the nested optimization of decentralized proximal ADMM for the case of GP modeling with the separable squared exponential kernel. Decentralized federated learning is promoted by prohibiting local data exchange between agents. The efficiency of the proposed method is illustrated with numerical experiments.},\n  month={December},\n  year={2023},\n  keywords={Gaussian processes, distributed optimization, multi-agent systems, decentralized networks},\n  url_pdf = {MRS23_Kontoudis_Decentralized_GP_Training.pdf},\n  url_video = {https://youtu.be/8Tz8ande5Gk?si=6tAo4m6hac2jsJdu},\n  doi = {10.1109/MRS60187.2023.10416790}\n}\n\n

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\n \n\n \n \n Xu, Z.; Kontoudis, G. P; and Vamvoudakis, K. G\n\n\n \n \n \n \n \n Online and Robust Intermittent Motion Planning in Dynamic and Changing Environments.\n \n \n \n \n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems (TNNLS). 2023.\n \n\n\n\n
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@article{xu2023TNNLS,\n  title={Online and Robust Intermittent Motion Planning in Dynamic and Changing Environments},\n  abstract = {In this paper, we propose RRT-Q$^{\\textrm{X}}_{\\infty}$, an online and intermittent kinodynamic motion planning framework for dynamic environments with unknown robot dynamics and unknown disturbances. We leverage RRT$^{\\textrm{X}}$ for global path planning and rapid replanning to produce waypoints as a sequence of boundary value problems (BVPs). For each BVP, we formulate a finite-horizon, continuous-time zero-sum game, where the control input is the minimizer, and the worst-case disturbance is the maximizer. We propose a \\textit{robust intermittent Q-learning} controller for waypoint navigation with completely unknown system dynamics, external disturbances, and intermittent control updates. We execute a relaxed persistence of excitation technique to guarantee that the Q-learning controller converges to the optimal controller. We provide rigorous Lyapunov-based proofs to guarantee the closed-loop stability of the equilibrium point. The effectiveness of the proposed RRT-Q$^{\\textrm{X}}_{\\infty}$ is illustrated with Monte-Carlo numerical experiments in numerous dynamic and changing environments.},\n  author={Xu, Zirui and Kontoudis, George P and Vamvoudakis, Kyriakos G},\n  journal={IEEE Transactions on Neural Networks and Learning Systems (TNNLS)},\n  year={2023},\n  keywords={motion planning, reinforcement learning, optimal control, event-trigger control, game theory},\n  url_pdf= {/publications/TNNLS23_Xu_OnlineRobustIntermittentMotionPlanningDynamicChangingEnvironments.pdf},\n  url_video = {https://youtu.be/iS_PzDmlpfs},\n  doi = {10.1109/TNNLS.2023.3303811}\n}\n\n

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\n \n\n \n \n Kontoudis, G. P; and Otte, M.\n\n\n \n \n \n \n \n Adaptive Exploration-Exploitation Active Learning of Gaussian Processes.\n \n \n \n \n\n\n \n\n\n\n In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2023. \n \n\n\n\n
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@inproceedings{kontoudis2023IROS,\n  title={Adaptive Exploration-Exploitation Active Learning of Gaussian Processes},\n  author={Kontoudis, George P and Otte, Michael},\n  booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},\n  abstract = {Active Learning of Gaussian process (GP) surrogates is an efficient way to model unknown environments in various applications. In this paper, we propose an adaptive exploration-exploitation active learning method (ALX) that can be executed rapidly to facilitate real-time decision making. For the exploration phase, we formulate an acquisition function that maximizes the approximated, expected Fisher information. For the exploitation phase, we employ a closed-form acquisition function that maximizes the total expected variance reduction of the search space. The determination of each phase is established with an exploration condition that measures the predictive accuracy of GP surrogates. Extensive numerical experiments in multiple input spaces validate the efficiency of our method.},\n  keywords={Gaussian processes, active learning, surrogates, gradient-based optimization},\n  month={October},\n  year={2023},\n  url_pdf = {IROS23_Kontoudis_AdaptiveActiveLearningALX.pdf},\n  url_video = {https://youtu.be/wK9QBAit0Nw?si=SlGsTrQuZPmuCQH1},\n  doi = {10.1109/IROS55552.2023.10342130}\n}\n\n

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\n \n\n \n \n Srivastava, A. K; Kontoudis, G. P; Sofge, D.; and Otte, M.\n\n\n \n \n \n \n \n Path-Based Sensors: Will the Knowledge of Correlation in Random Variables Accelerate Information Gathering?.\n \n \n \n \n\n\n \n\n\n\n IEEE International Conference on Robotics and Automation (ICRA), Workshop on Communication Challenges in Multi-Robot Systems: Perception, Coordination, and Learning, May 2023.\n \n\n\n\n
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@misc{srivastava2022ICRAWS,\n  title={Path-Based Sensors: Will the Knowledge of Correlation in Random Variables Accelerate Information Gathering?},\n  author={Srivastava, Alkesh K and Kontoudis, George P and Sofge, Donald and Otte, Michael},\n  howpublished = {IEEE International Conference on Robotics and Automation (ICRA), Workshop on Communication Challenges in Multi-Robot Systems: Perception, Coordination, and Learning},\n  abstract = {Effective communication is crucial for deploying robots in mission-specific tasks, but inadequate or unreliable communication can greatly reduce mission efficacy, for example in search and rescue missions where communication-denied conditions may occur. In such missions, robots are deployed to locate targets, such as human survivors, but they might get trapped at hazardous locations, such as in a trapping pit or by debris. Thus, the information the robot collected is lost owing to the lack of communication. In our prior work, we developed the notion of a path-based sensor. A path-based sensor detects whether or not an event has occurred along a particular path, but it does not provide the exact location of the event. Such path-based sensor observations are well-suited to communication-denied environments, and various studies have explored methods to improve information gathering in such settings. In some missions it is typical for target elements to be in close proximity to hazardous factors that hinder the information-gathering process. In this study, we examine a similar scenario and conduct experiments to determine if additional knowledge about the correlation between hazards and targets improves the efficiency of information gathering. To incorporate this knowledge, we utilize a Bayesian network representation of domain knowledge and develop an algorithm based on this representation. Our empirical investigation reveals that such additional information on correlation is beneficial only in environments with moderate hazard lethality, suggesting that while knowledge of correlation helps, further research and\ndevelopment is necessary for optimal outcomes.},\n  month={May},\n  year={2023},\n  keywords={path planning, multi-agent systems, bayesian inference},\n  url_pdf = {https://arxiv.org/pdf/2305.06929.pdf},\n  url_html = {https://arxiv.org/abs/2305.06929}\n}\n\n

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\n \n\n \n \n Kontoudis, G. P; and Otte, M.\n\n\n \n \n \n \n \n Closed-Form Active Learning using Expected Variance Reduction of Gaussian Process Surrogates for Adaptive Sampling.\n \n \n \n \n\n\n \n\n\n\n In American Control Conference (ACC), pages 4626–4632, May 2023. \n \n\n\n\n
\n\n\n\n \n \n $\"Closed-Form$ pdf\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{kontoudis2023ACC,\n  title={Closed-Form Active Learning using Expected Variance Reduction of Gaussian Process Surrogates for Adaptive Sampling},\n  author={Kontoudis, George P and Otte, Michael},\n  booktitle={American Control Conference (ACC)},\n  abstract = {Adaptive sampling of latent fields remains a challenging task, especially in high-dimensional input spaces. In this paper, we propose an active learning method of expected variance reduction with Gaussian process (GP) surrogates using a closed-form gradient. The use of closed-form gradient leads the optimization to find better solutions with reduced computations. We derive the closed-form gradient for active learning Cohn (ALC) using GP surrogates that are formed with the separable squared exponential covariance function. Moreover, we provide algorithmic details for the execution of the closed form ALC (cALC). Numerical experiments with multiple input space dimensions illustrate the efficacy of our method.},\n  keywords={Gaussian processes, active learning, surrogates, gradient-based optimization},\n  pages = {4626--4632},\n  month={May},\n  year={2023},\n  url_pdf = {ACC23_Kontoudis_ClosedFormActiveLearningExpectedVarianceReduction.pdf},\n  doi = {10.23919/ACC55779.2023.10156350}\n}\n\n

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\n \n 2022\n \n \n (5)\n \n \n

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\n \n\n \n \n Srivastava, A. K; Kontoudis, G. P; Sofge, D.; and Otte, M.\n\n\n \n \n \n \n \n Distributed Multi-Robot Information Gathering using Path-Based Sensors in Entropy-Weighted Voronoi Regions.\n \n \n \n \n\n\n \n\n\n\n In International Symposium on Distributed Autonomous Robotic Systems (DARS), November 2022. \n \n\n\n\n
\n\n\n\n \n \n $\"Distributed$ pdf\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{srivastava2022DARS,\n  title={Distributed Multi-Robot Information Gathering using Path-Based Sensors in Entropy-Weighted Voronoi Regions},\n  author={Srivastava, Alkesh K and Kontoudis, George P and Sofge, Donald and Otte, Michael},\n  booktitle={International Symposium on Distributed Autonomous Robotic Systems (DARS)},\n  abstract = {In this paper, we present a distributed information-gathering algorithm for multi-robot systems that use multiple path-based sensors to infer the locations of hazards within the environment. Path-based sensors output binary observations, reporting whether or not an event (like robot destruction) has occurred somewhere along a path, but without the ability to discern where along a path an event has occurred. Prior work has shown that path-based sensors can be used for search and rescue in hazardous communication-denied environments---sending robots into the environment one-at-a-time. We extend this idea to enable multiple robots to search the environment simultaneously. The search space contains targets (human survivors) amidst hazards that can destroy robots (triggering a path-based hazard sensor). We consider a case where communication from the unknown feld is prohibited due to communication loss, jamming, or stealth. The search eﬀort is distributed among multiple robots using an entropy-weighted Voronoi partitioning of the environment, such that during each search round all regions have approximately equal information entropy. In each round, every robot is assigned a region in which its search path is calculated. Numerical Monte Carlo simulations are used to compare this idea to other ways of using path-based sensors on multiple robots. The experiments show that dividing search eﬀort using entropy-weighted Voronoi partitioning outperforms the other methods in terms of the information gathered and computational cost.},\n  month={November},\n  year={2022},\n  keywords={path planning, multi-agent systems,bayesian inference},\n  url_pdf = {DARS22_Srivastava_DistributedMultiRobotInformationGatheringPathBasedSensorsEntropyWeightedVoronoi.pdf}\n}\n\n

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\n \n\n \n \n Mavridis, C. N; Kontoudis, G. P; and Baras, J. S\n\n\n \n \n \n \n \n Sparse Gaussian Process Regression using Progressively Growing Learning Representations.\n \n \n \n \n\n\n \n\n\n\n In IEEE Conference on Decision and Control (CDC), pages 1454–1459, December 2022. \n \n\n\n\n
\n\n\n\n \n \n $\"Sparse$ pdf\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{mavridis2022CDC,\n  title={Sparse Gaussian Process Regression using Progressively Growing Learning Representations},\n  author={Mavridis, Christos N and Kontoudis, George P and Baras, John S},\n  booktitle={IEEE Conference on Decision and Control (CDC)},\n  abstract = {We present a new sparse Gaussian process regression model whose covariance function is parameterized by the locations of a progressively growing set of pseudo-inputs generated by an online deterministic annealing optimization algorithm. A series of entropy-regularized optimization problems is solved sequentially, introducing a bifurcation phenomenon, according to which, pseudo-inputs are gradually generated. This results in an active learning approach, which, in contrast to most existing works, can modify already selected pseudoinputs and is trained using a recursive gradient-free stochastic approximation algorithm. Finally, the proposed algorithm is able to incorporate prior knowledge in the form of a probability density, according to which new observations are sampled. Experimental results showcase the effcacy and potential advantages of the proposed methodology.},\n  keywords={Gaussian processes, active learning,gradient-free optimization},\n  pages = {1454--1459},\n  month={December},\n  year={2022},\n  keywords={Gaussian processes, active learning, gradient-free optimization},\n  url_pdf = {CDC22_Mavridis_SparseGaussianProcessRegressionProgressivelyGrowingDatasets.pdf},\n  doi = {10.1109/CDC51059.2022.9992933}\n}\n\n

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\n \n\n \n \n Kontoudis, G. P\n\n\n \n \n \n \n \n Scalable Multi-Robot Active Exploration.\n \n \n \n \n\n\n \n\n\n\n Robotics: Science and Systems (RSS) Pioneers Workshop, June 2022.\n \n\n\n\n
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@misc{kontoudis2022scalable,\n  title = {Scalable Multi-Robot Active Exploration},\n  author = {Kontoudis, George P},\n  howpublished = {Robotics: Science and Systems (RSS) Pioneers Workshop},\n  keywords={Gaussian processes, distributed optimization, wireless communications, decentralized networks, multi-agent systems, motion planning, reinforcement learning, optimal control, active learning},\n  month={June},\n  year = {2022},\n  url_pdf = {Pioneers22_Kontoudis_Scalable_Multi_Robot_Active_Exploration.pdf},\n  url_html = {https://sites.google.com/view/rsspioneers2022/}\n}\n\n

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\n \n\n \n \n Kontoudis, G. P; and Stilwell, D. J\n\n\n \n \n \n \n \n Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning.\n \n \n \n \n\n\n \n\n\n\n arXiv preprint arXiv:2203.02865, 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"Fully$ pdf\n \n \n \n $\"Fully$ html\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 8 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@misc{kontoudis2022fully,\n  title = {Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning},\n  author = {Kontoudis, George P and Stilwell, Daniel J},\n  abstract = {In this paper, we propose decentralized and scalable algorithms for Gaussian process (GP) training and prediction in multi-agent systems. To decentralize the implementation of GP training optimization algorithms, we employ the alternating direction method of multipliers (ADMM). A closed-form solution of the decentralized proximal ADMM is provided for the case of GP hyper-parameter training with maximum likelihood estimation. Multiple aggregation techniques for GP prediction are decentralized with the use of iterative and consensus methods. In addition, we propose a covariance-based nearest neighbor selection strategy that enables a subset of agents to perform predictions. The efficacy of the proposed methods is illustrated with numerical experiments on synthetic and real data.},\n  keywords={Gaussian processes, distributed optimization, multi-agent systems, decentralized networks,gradient-based optimization},\n  howpublished = {arXiv preprint arXiv:2203.02865},\n  year = {2022},\n  url_pdf = {https://arxiv.org/pdf/2203.02865.pdf},\n  url_html = {https://arxiv.org/abs/2203.02865}\n}\n\n

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\n \n\n \n \n Kontoudis, G. P; Vamvoudakis, K. G; and Xu, Z.\n\n\n \n \n \n \n \n RRT-QX: Real-Time Kinodynamic Motion Planning in Dynamic Environments with Continuous-Time Reinforcement Learning.\n \n \n \n \n\n\n \n\n\n\n In Wei, B., editor(s), Brain and Cognitive Intelligence: Control in Robotics. Taylor & Francis Group, CRC Press, 2022.\n \n\n\n\n
\n\n\n\n \n \n $\"RRT-QX:$ pdf\n \n \n \n $\"RRT-QX:$ video\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 7 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@incollection{Kontoudis2022RRTQX,\n\ttitle={RRT-QX: Real-Time Kinodynamic Motion Planning in Dynamic Environments with Continuous-Time Reinforcement Learning},\n\tauthor={Kontoudis, George P and Vamvoudakis, Kyriakos G and Xu, Zirui},\n\teditor      = {Wei, Bin},\n\tbooktitle   = {Brain and Cognitive Intelligence: Control in Robotics},\n\tpublisher   = {Taylor \\& Francis Group, CRC Press},\n\tabstract = {This chapter presents a real-time kinodynamic motion planning technique for linear systems with completely unknown dynamics in environments with unpredictable obstacles. The methodology incorporates: i) a sampling-based algorithm for path planning and fast replanning; and ii) continuous-time Q-learning for the solution of finite-horizon optimal control problems in real-time. The path planner produces a set of waypoints that dynamically change in time according to the unpredictably appearing obstacles, while the Q-learning controller is responsible for optimal waypoint navigation. The efficacy of the methodology has been validated with simulations. },\n\tyear={2022},\n\tkeywords={motion planning, reinforcement learning, optimal control},\n\turl_pdf = {Chap22_Kontoudis_RRTQX_RealTimeKinodynamicMotionPlanningDynamicEnvironments.pdf},\n\turl_video = {https://youtu.be/Sxu04gSdsEA},\n\tdoi = {10.1201/9781003050315-1}\n}\n\n

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\n \n\n \n \n Kontoudis, G. P\n\n\n \n \n \n \n \n Communication-Aware, Scalable Gaussian Processes for Decentralized Exploration.\n \n \n \n \n\n\n \n\n\n\n thesis, Virginia Tech, 2021.\n \n\n\n\n
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@thesis{Kontoudis2021communication,\n    author    = {Kontoudis, George P},\n    title     = {Communication-Aware, Scalable Gaussian Processes for Decentralized Exploration},\n    school    = {Virginia Tech},\n    year      = {2021},\n    keywords={Gaussian processes, spatial statistics, distributed optimization, underwater vehicles, wireless communications, decentralized networks, multi-agent systems},\n    url_pdf  = {Kontoudis_GP_D_2021.pdf},\n    url_html = {https://vtechworks.lib.vt.edu/handle/10919/107923}\n}\n\n

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\n \n\n \n \n Netter, J.; Kontoudis, G. P; and Vamvoudakis, K. G\n\n\n \n \n \n \n \n Bounded Rational RRT-QX: Multi-Agent Motion Planning in Dynamic Human-Like Environments Using Cognitive Hierarchy and Q-Learning.\n \n \n \n \n\n\n \n\n\n\n In IEEE Conference on Decision and Control (CDC), pages 3597–3602, December 2021. \n \n\n\n\n
\n\n\n\n \n \n $\"Bounded$ pdf\n \n \n \n $\"Bounded$ video\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{Netter2021CDC,\n  title={Bounded Rational RRT-QX: Multi-Agent Motion Planning in Dynamic Human-Like Environments Using Cognitive Hierarchy and Q-Learning},\n  author={Netter, Josh and Kontoudis, George P and Vamvoudakis, Kyriakos G},\n  abstract = {This paper presents a multi-agent motion planning algorithm for human-like navigation in dynamic environments. A cognitive hierarchy approach is used to model the motion of autonomous agents. We discuss potential levels of rationality and introduce a method to predict them in real-time. The rationality level prediction is achieved by observing the kinodynamic distance (KD) of other agents. An offline training phase is required to learn the maximum KD from multiple boundary value problems. Collision avoidance is ensured by introducing artificial obstacles in the environment based on the predicted levels of rationality. The motion planning is then carried out using RRT-QX. The effectiveness of the bounded rational motion planning algorithm is illustrated in simulations.},\n  booktitle={IEEE Conference on Decision and Control (CDC)},\n  pages = {3597--3602},\n  month={December},\n  year={2021},\n  keywords={motion planning, reinforcement learning, optimal control, game theory},\n  url_pdf  = {CDC21_Netter_Bounded_Rational_RRTQX.pdf},\n  url_video = {https://youtu.be/7nBL1g67RKE},\n  doi = {10.1109/CDC45484.2021.9683761}\n}\n\n

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\n \n\n \n \n Gao, G.; Shahmohammadi, M.; Gerez, L.; Kontoudis, G. P; and Liarokapis, M.\n\n\n \n \n \n \n \n On Differential Mechanisms for Underactuated, Lightweight, Adaptive Prosthetic Hands.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Neurorobotics, 15: 106. October 2021.\n \n\n\n\n
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@article{Gao2021FRONTIERS,\n  title = {On Differential Mechanisms for Underactuated, Lightweight, Adaptive Prosthetic Hands},\n  author={Gao, Geng and Shahmohammadi, Mojtaba and Gerez, Lucas and Kontoudis, George P and Liarokapis, Minas},\n  abstract = {Over the last decade underactuated, adaptive robot grippers and hands have received an increased interest from the robotics research community. This class of robotic end-effectors can be used in many different fields and scenarios with a very promising application being the development of prosthetic devices. Their suitability for the development of such devices is attributed to the utilization of underactuation that provides increased functionality and dexterity with reduced weight, cost, and control complexity. The most critical components of underactuated, adaptive hands that allow them to perform a broad set of grasp poses are appropriate differential mechanisms that facilitate the actuation of multiple degrees of freedom using a single motor. In this work, we focus on the design, analysis, and experimental validation of a four output geared differential, a series elastic differential, and a whiffletree differential that can incorporate a series of manual and automated locking mechanisms. The locking mechanisms have been developed so as to enhance the control of the differential outputs, allowing for efficient grasp selection with a minimal set of actuators. The differential mechanisms are applied to prosthetic hands, comparing them and describing the benefits and the disadvantages of each.},\n  journal={Frontiers in Neurorobotics},\n  volume={15},\n  pages={106},\n  month = {October},\n  year={2021},\n  keywords={differential mechanisms, robot hands, tendon-driven mechanisms},\n  publisher={Frontiers},\n  url_pdf  = {https://www.frontiersin.org/articles/10.3389/fnbot.2021.702031/full},\n  doi = {10.3389/fnbot.2021.702031}\n}\n\n

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\n \n\n \n \n Kontoudis, G. P; and Stilwell, D. J\n\n\n \n \n \n \n \n Decentralized Nested Gaussian Processes for Multi-Robot Systems.\n \n \n \n \n\n\n \n\n\n\n In IEEE International Conference on Robotics and Automation (ICRA), pages 8881–8887, June 2021. \n \n\n\n\n
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@inproceedings{Kontoudis2021ICRA,\n  title = {Decentralized Nested Gaussian Processes for Multi-Robot Systems},\n  author={Kontoudis, George P and Stilwell, Daniel J},\n  abstract = {In this paper, we propose two decentralized approximate algorithms for nested Gaussian processes in multirobot systems. The distributed implementation is achieved with iterative and consensus methods that facilitate local computations at the expense of inter-robot communications. Moreover, we propose a covariance-based nearest neighbor robot selection strategy that enables a subset of agents to perform predictions. In addition, both algorithms are proved to be consistent. Empirical evaluations with real data illustrate the efficiency of the proposed algorithms.},\n  booktitle={IEEE International Conference on Robotics and Automation (ICRA)},\n  pages={8881--8887},\n  year={2021},\n  month = {June},\n  keywords={multi-agent systems, Gaussian processes, decentralized networks},\n  url_pdf  = {ICRA21_Kontoudis_DistributedNestedGaussianProcesses.pdf},\n  url_video = {https://youtu.be/uHK5e-EFKEU},\n  url_code = {https://github.com/gkontoudis/decentralized-GP},\n  doi = {10.1109/ICRA48506.2021.9561566}\n}\n\n

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\n \n\n \n \n Liarokapis, M.; and Kontoudis, G. P\n\n\n \n \n \n \n \n Teaching Robotic and Biomechatronic Concepts with a Gripper Design Project and a Grasping and Manipulation Competition.\n \n \n \n \n\n\n \n\n\n\n In IEEE International Conference on Robotics and Automation (ICRA), pages 2576–2582, June 2021. \n \n\n\n\n
\n\n\n\n \n \n $\"Teaching$ pdf\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 6 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{Liarokapis2021ICRA,\n  title = {Teaching Robotic and Biomechatronic Concepts with a Gripper Design Project and a Grasping and Manipulation Competition},\n  author={Liarokapis, Minas and Kontoudis, George P},\n  abstract = {Lecturers of Engineering courses around the world are struggling to increase the engagement of students through the introduction of appropriate hands-on activities and assignments. In Biomechatronics and Robotics courses these assignments typically focus on how certain devices are designed, modelled, fabricated, or controlled. The hardware for these assignments is usually purchased by some external vendor and the students only get the chance to analyze it or program it, so as to execute a useful task (e.g., programming mobile robots to perform path following tasks). Student engagement can be increased by instructing the students to prepare the hardware for their assignment. This also increases the sense of ownership of the project outcomes. In this paper, we present how a robotic gripper / hand design project and the introduction of a grasping and manipulation competition as a course assignment, can significantly increase the student engagement and their understanding of the taught concepts. The presented best practices have been trialed over the last four years in two different courses (one undergraduate and one postgraduate) of the Department of Mechanical Engineering at the University of Auckland in New Zealand. For the particular assignment the students were asked to fully develop a robotic gripper or hand from scratch using a single actuator (only the actuator and the power electronics were provided). The performance of the developed devices was assessed through the participation in a grasping and manipulation competition. All the details of the proposed assignment are presented, hoping that they could help other lecturers and teachers to prepare similar activities.},\n  booktitle={IEEE International Conference on Robotics and Automation (ICRA)},\n  pages={2576--2582},\n  year={2021},\n  month = {June},\n  keywords={teaching, tendon-driven mechanisms, robot hands},\n  url_pdf  = {ICRA21_Liarokapis_TeachingRoboticGraspingManipulation.pdf},\n  doi = {10.1109/ICRA48506.2021.9561114}\n}\n\n

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\n \n\n \n \n Kontoudis, G. P; and Stilwell, D. J\n\n\n \n \n \n \n \n Prediction of Acoustic Communication Performance in Marine Robots Using Model-Based Kriging.\n \n \n \n \n\n\n \n\n\n\n In American Control Conference (ACC), pages 3779–3786, May 2021. \n \n\n\n\n
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@inproceedings{Kontoudis2021ACC,\n  title = {Prediction of Acoustic Communication Performance in Marine Robots Using Model-Based Kriging},\n  author={Kontoudis, George P and Stilwell, Daniel J},\n  abstract = {In this paper, we present a data-driven iterative algorithm for accurate prediction of underwater acoustic communication performance at unvisited sites. The prediction algorithm consists of two steps: i) estimation of the covariance matrix; and ii) prediction of the communication performance. The importance of the covariance estimation is highlighted with a multi-stage, model-based iterative methodology that produces unbiased and robust results. The efficiency of the framework has been validated with synthetic data.},\n  booktitle={American Control Conference (ACC)},\n  pages={3779--3786},\n  year={2021},\n  month = {May},\n  keywords={underwater vehicles, wireless communications, spatial statistics},\n  url_pdf  = {ACC21_Kontoudis_PredictionUWACommunicationPerformance.pdf},\n  doi = {10.23919/ACC50511.2021.9483186}\n}\n\n

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\n \n\n \n \n Kontoudis, G. P; Krauss, S.; and Stilwell, D. J\n\n\n \n \n \n \n \n Model-Based Learning of Underwater Acoustic Communication Performance for Marine Robots.\n \n \n \n \n\n\n \n\n\n\n Robotics and Autonomous Systems (RAS), 142: 103811. 2021.\n \n\n\n\n
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@article{Kontoudis2021RAS,\n  title={Model-Based Learning of Underwater Acoustic Communication Performance for Marine Robots},\n  author={Kontoudis, George P and Krauss, Stephen and Stilwell, Daniel J},\n  journal={Robotics and Autonomous Systems (RAS)},\n\tvolume = {142},\n\tpages = {103811},\n  publisher={Elsevier},\n  abstract = {Accurate prediction of acoustic communication performance is an important capability for marine robots. In this paper, we propose a model-based learning methodology for the prediction of underwater acoustic communication performance. The learning algorithm consists of two steps: i) estimation of the covariance matrix by evaluating candidate functions with estimated parameters; and ii) prediction of communication performance. Covariance estimation is addressed with a multi-stage iterative training method that produces unbiased and robust results with nested models. The efficiency of the framework is validated with simulations and experimental data from field trials. The field trials involved a manned surface vehicle and an autonomous underwater vehicle.},\n  year={2021},\n  keywords={underwater vehicles, wireless communications, spatial statistics},\n  url_pdf = {RAS_accepted_Kontoudis_Prediction_of_Communication_Performance.pdf},\n  doi = {10.1016/j.robot.2021.103811}\n}\n\n\n

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\n \n 2020\n \n \n (2)\n \n \n

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\n \n\n \n \n Gorjup, G.; Kontoudis, G. P; Dwivedi, A.; Gao, G.; Matsunaga, S.; Mariyama, T.; MacDonald, B.; and Liarokapis, M.\n\n\n \n \n \n \n \n Combining Programming by Demonstration with Path Optimization and Local Replanning to Facilitate the Execution of Assembly Tasks.\n \n \n \n \n\n\n \n\n\n\n In IEEE International Conference on Systems, Man and Cybernetics (SMC), pages 1885–1892, October 2020. \n \n\n\n\n
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@inproceedings{Gorjup2020SMC,\n  title={Combining Programming by Demonstration with Path Optimization and Local Replanning to Facilitate the Execution of Assembly Tasks},\n  abstract = {With the emergence of agile manufacturing in highly automated industrial environments, the demand for efficient robot adaptation to dynamic task requirements is increasing. For assembly tasks in particular, classic robot programming methods tend to be rather time intensive. Thus, effectively responding to rapid production changes requires faster and more intuitive robot teaching approaches. This work focuses on combining programming by demonstration with path optimization and local replanning methods to allow for fast and intuitive programming of assembly tasks that requires minimal user expertise. Two demonstration approaches have been developed and integrated in the framework, one that relies on human to robot motion mapping (teleoperation based approach) and a kinesthetic teaching method. The two approaches have been compared with the classic, pendant based teaching. The framework optimizes the demonstrated robot trajectories with respect to the detected obstacle space and the provided task specifications and goals. The framework has also been designed to employ a local replanning scheme that adjusts the optimized robot path based on online feedback from the camera-based perception system, ensuring collision-free navigation and the execution of critical assembly motions. The efficiency of the methods has been validated through a series of experiments involving the execution of assembly tasks. Extensive comparisons of the different demonstration methods have been performed and the approaches have been evaluated in terms of teaching time, ease of use, and path length.},\n  author={Gorjup, Gal and Kontoudis, George P and Dwivedi, Anany and Gao, Geng and Matsunaga, Saori and Mariyama, Toshisada and MacDonald, Bruce and Liarokapis, Minas},\n  booktitle={IEEE International Conference on Systems, Man and Cybernetics (SMC)},\n  pages={1885--1892},\n  month={October},\n  year={2020},\n  keywords={motion planning, programming by demonstration},\n  url_pdf = {SMC20_Gorjup_CombiningPbdPathPlanning.pdf},\n  url_video = {https://youtu.be/3r-ndmtI6gk},\n  url_page = {http://www.newdexterity.org/skilltransfer/},\n  doi = {10.1109/SMC42975.2020.9282991}\n}\n\n

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\n \n\n \n \n Kontoudis, G. P; Xu, Z.; and Vamvoudakis, K. G\n\n\n \n \n \n \n \n Online, Model-Free Motion Planning in Dynamic Environments: An Intermittent, Finite Horizon Approach with Continuous-Time Q-Learning.\n \n \n \n \n\n\n \n\n\n\n In American Control Conference (ACC), pages 3873–3878, July 2020. \n \n\n\n\n
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@inproceedings{Kontoudis2020ACC,\n  title={Online, Model-Free Motion Planning in Dynamic Environments: An Intermittent, Finite Horizon Approach with Continuous-Time Q-Learning},\n  abstract = {This paper presents an online kinodynamic motion planning scheme for dynamically evolving environments, by employing Q-learning. The methodology addresses the finite horizon continuous-time optimal control problem with completely unknown system dynamics. An actor-critic structure is employed along with a buffer of previous experiences, to approximate the optimal policy and alleviate the learning signal requirements. The methodology is equipped with a terminal state evaluation to achieve fast navigation. The path planning is assigned to the RRTX. An obstacle augmentation and a local re-planning strategy are responsible for collision-free navigation. Rigorous Lyapunov-based proofs are provided to guarantee closed-loop stability of the equilibrium point. We evaluate the efficacy of the methodology with simulations.},\n  author={Kontoudis, George P and Xu, Zirui and Vamvoudakis, Kyriakos G},\n  booktitle={American Control Conference (ACC)},\n  pages={3873--3878},\n  month={July},\n  year={2020},\n  keywords={motion planning, reinforcement learning, optimal control, event-trigger control},\n  url_pdf = {ACC20_Kontoudis_KinodynamicMotionPlanningDynamicEnvironmentsIntermittentQLearning.pdf},\n  url_video = {https://youtu.be/Sxu04gSdsEA},\n  url_3mt = {https://youtu.be/G6rjcidildA},\n  doi = {10.23919/ACC45564.2020.9148047}\n}\n\n

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\n \n\n \n \n Kontoudis, G. P; and Vamvoudakis, K. G\n\n\n \n \n \n \n \n Kinodynamic Motion Planning With Continuous-Time Q-Learning: An Online, Model-Free, and Safe Navigation Framework.\n \n \n \n \n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 30(12): 3803–3817. December 2019.\n \n\n\n\n
\n\n\n\n \n \n $\"Kinodynamic$ pdf\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 11 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@article{Kontoudis2019TNNLS,\n  title={Kinodynamic Motion Planning With Continuous-Time Q-Learning: An Online, Model-Free, and Safe Navigation Framework},\n  abstract = {This paper presents an online kinodynamic motion planning algorithmic framework using asymptotically optimal rapidly-exploring random tree (RRT*) and continuous-time Q-learning, which we term as RRT-Q*. We formulate a model-free Q-based advantage function and we utilize integral reinforcement learning to develop tuning laws for the online approximation of the optimal cost and the optimal policy of continuous-time linear systems. Moreover, we provide rigorous Lyapunov-based proofs for the stability of the equilibrium point, which results in asymptotic convergence properties. A terminal state evaluation procedure is introduced to facilitate the online implementation. We propose a static obstacle augmentation and a local replanning framework, which are based on topological connectedness, to locally recompute the robot's path and ensure collision-free navigation. We perform simulations and a qualitative comparison to evaluate the efficacy of the proposed methodology.},\n  author={Kontoudis, George P and Vamvoudakis, Kyriakos G},\n  journal={IEEE Transactions on Neural Networks and Learning Systems (TNNLS)},\n  year={2019},\n  volume={30},\n  number={12},\n  pages={3803--3817},\n  publisher={IEEE},\n  keywords={motion planning, reinforcement learning, optimal control},\n  url_pdf =    {TNNLS19_Kontoudis_KinodynamicMotionPlanningWithContinuousTimeQLearning.pdf},\n  doi = {10.1109/TNNLS.2019.2899311},\n  month = {December}\n}\n%   note = {[2019 Impact factor: 11.683]}\n\n

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\n \n\n \n \n Kontoudis, G. P; and Stilwell, D. J\n\n\n \n \n \n \n \n A Comparison of Kriging and Cokriging for Estimation of Underwater Acoustic Communication Performance.\n \n \n \n \n\n\n \n\n\n\n In ACM International Conference on Underwater Networks & Systems (WuWNet), pages 1–8, October 2019. \n \n\n\n\n
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@inproceedings{Kontoudis2019WUWNET,\n  title={A Comparison of Kriging and Cokriging for Estimation of Underwater Acoustic Communication Performance},\n  abstract = {Mobile underwater communication network nodes, such as autonomous underwater vehicles, can use estimates of underwater acoustic communication performance to anticipate where they are likely to be connected to the communication network. In this paper, we consider the challenge of estimating a spatial field that represents underwater acoustic communication performance from a set of measurements. Kriging, which is widely used in geostatistics, has been previously used to estimate the communication performance at unknown locations, by performing spatial extrapolation. We compare kriging to cokriging where the latter is a bivariate estimation method. The methodology yields estimates of communication performance at desired locations based on measurements acquired at other locations. Moreover, a variance measure is provided that characterizes the uncertainty of the estimation. We present the structure of the proposed estimation technique and its computational complexity. We evaluate the efficacy of the technique by considering an approximate linear-log model of the communication performance, environmental noise, and a direct comparison of kriging and cokriging results. We provide two sets of simulations in which the proposed multivariate cokriging framework outperforms the univariate kriging in the estimation process.},\n  author={Kontoudis, George P and Stilwell, Daniel J},\n  booktitle={ACM International Conference on Underwater Networks \\& Systems (WuWNet)},\n  pages={1--8},\n  year={2019},\n  month = {October},\n  keywords={underwater vehicles, wireless communications, spatial statistics},\n  url_pdf  = {WUWNet19_Kontoudis_ComparisonKrigingCokriging_CommunicationPerformance.pdf},\n  url_slides = {http://www.georgekontoudis.com/presentations/wuwnet19_kontoudis_ComarisonKrigingCokriging.pdf},\n  doi = {10.1145/3366486.3366515}\n}\n\n%   note = {[Full-length, acceptance rate 38\\%]},\n\n

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\n \n\n \n \n Kontoudis, G. P; Liarokapis, M.; and Vamvoudakis, K. G\n\n\n \n \n \n \n \n An Adaptive, Humanlike Robot Hand with Selective Interdigitation: Towards Robust Grasping and Dexterous, In-Hand Manipulation.\n \n \n \n \n\n\n \n\n\n\n In IEEE-RAS International Conference on Humanoid Robots (Humanoids), pages 251–258, October 2019. \n \n\n\n\n
\n\n\n\n \n \n $\"An$ pdf\n \n \n \n $\"An$ slides\n \n \n \n $\"An$ video\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{Kontoudis2019Humanoids,\n  title={An Adaptive, Humanlike Robot Hand with Selective Interdigitation: Towards Robust Grasping and Dexterous, In-Hand Manipulation},\n  abstract = {This paper presents an adaptive robot hand that is capable of performing selective interdigitation, robust grasping, and dexterous, in-hand manipulation. The design consists of underactuated, compliant, anthropomorphic robot fingers that are implemented with flexure joints based on elastomer materials (urethane rubber). The metacarpophalangeal (MCP) joint of each finger can achieve both flexion/extension and abduction/adduction. The use of differential mechanisms simplifies the actuation scheme, as we utilize only two actuators for four fingers, achieving affordable dexterity. The two actuators offer increased power transmission during the execution of grasping and manipulation tasks. The importance of the thumb is highlighted with the use of two individual tendon-routing systems for its control. An analytical model is employed to derive the rotational stiffness of the finger flexure joints and select appropriate actuators. Selective interdigitation allows the robot hand to switch from pinch grasp configurations to power grasp configurations optimizing the performance of the device for specific objects. The design can be fabricated with off-the-shelf materials and rapid prototyping techniques, while its efficiency has been validated using an extensive set of experimental paradigms that involved the execution of complex tasks with everyday life objects.},\n  author={Kontoudis, George P and Liarokapis, Minas and Vamvoudakis, Kyriakos G},\n  booktitle={IEEE-RAS International Conference on Humanoid Robots (Humanoids)},\n  pages={251--258},\n  keywords = {tendon-driven mechanisms, differential mechanisms, robot hands},\n  month = {October},\n  year={2019},\n  url_pdf  = {Humanoids19_Kontoudis_Adaptive_Humanlike_Robot_Hand.pdf},\n  url_slides = {http://www.georgekontoudis.com/presentations/humanoids19_kontoudis_AdaptiveHands_InHandManipulation.pdf},\n  url_video = {https://youtu.be/wvo0tKD7eJ8},\n  doi = {10.1109/Humanoids43949.2019.9035037}\n}\n\n

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\n \n\n \n \n Kontoudis, G. P; and Vamvoudakis, K. G\n\n\n \n \n \n \n \n Robust Kinodynamic Motion Planning using Model-Free Game-Theoretic Learning.\n \n \n \n \n\n\n \n\n\n\n In American Control Conference (ACC), pages 273–278, July 2019. \n \n\n\n\n
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@inproceedings{Kontoudis2019ACC,\n  title={Robust Kinodynamic Motion Planning using Model-Free Game-Theoretic Learning},\n  abstract = {This paper presents an online, robust, and model-free motion planning framework for kinodynamic systems. In particular, we employ a Q-learning algorithm for a two player zero-sum dynamic game to account for worst-case disturbances and kinodynamic constraints. We use one critic, and two actor approximators to solve online the finite horizon minimax problem with a form of integral reinforcement learning. We then leverage a terminal state evaluation structure to facilitate the online implementation. A static obstacle augmentation, and a local replanning framework is presented to guarantee safe kinodynamic motion planning. Rigorous Lyapunov-based proofs are provided to guarantee closed-loop stability, while maintaining robustness and optimality. We finally evaluate the efficacy of the proposed framework with simulations and we provide a qualitative comparison of kinodynamic motion planning techniques.},\n  author={Kontoudis, George P and Vamvoudakis, Kyriakos G},\n  booktitle={American Control Conference (ACC)},\n  pages={273--278},\n  month={July},\n  year={2019},\n  keywords={motion planning, reinforcement learning, optimal control, game theory},\n  url_pdf  = {ACC19_Kontoudis_RobustKinodynamicMotionPlanning_ModelFreeGameTheoreticLearning.pdf},\n  url_slides = {http://www.georgekontoudis.com/presentations/acc19_kontoudis_regularPresentation.pdf},\n  url_video = {https://youtu.be/N5cvOxQXMcI},\n  doi = {10.23919/ACC.2019.8814941}\n}\n\n

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\n \n\n \n \n Kontoudis, G. P; Liarokapis, M.; Vamvoudakis, K. G; and Furukawa, T.\n\n\n \n \n \n \n \n An Adaptive Actuation Mechanism for Anthropomorphic Robot Hands.\n \n \n \n \n\n\n \n\n\n\n Frontiers in Robotics and AI, 6: 1–16. July 2019.\n \n\n\n\n
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@article{Kontoudis2019FRONTIERS,\n  title={An Adaptive Actuation Mechanism for Anthropomorphic Robot Hands},\n  abstract = {This paper presents an adaptive actuation mechanism that can be employed for the development of anthropomorphic, dexterous robot hands. The tendon-driven actuation mechanism achieves both flexion/extension and adduction/abduction on the finger's metacarpophalangeal joint using two actuators. Moment arm pulleys are employed to drive the tendon laterally and achieve a simultaneous execution of abduction and flexion motion. Particular emphasis has been given to the modeling and analysis of the actuation mechanism. More specifically, the analysis determines specific values for the design parameters for desired abduction angles. Also, a model for spatial motion is provided that relates the actuation modes with the finger motions. A static balance analysis is performed for the computation of the tendon force at each joint. A model is employed for the computation of the stiffness of the rotational flexure joints. The proposed mechanism has been designed and fabricated with the hybrid deposition manufacturing technique. The efficiency of the mechanism has been validated with experiments that include the assessment of the role of friction, the computation of the reachable workspace, the assessment of the force exertion capabilities, the demonstration of the feasible motions, and the evaluation of the grasping and manipulation capabilities. An anthropomorphic robot hand equipped with the proposed actuation mechanism was also fabricated to evaluate its performance. The proposed mechanism facilitates the collaboration of actuators to increase the exerted forces, improving hand dexterity and allowing the execution of dexterous manipulation tasks.},\n  author={Kontoudis, George P and Liarokapis, Minas and Vamvoudakis, Kyriakos G and Furukawa, Tomonari},\n  journal={Frontiers in Robotics and AI},\n  volume={6},\n  pages={1--16},\n  month = {July},\n  year={2019},\n  keywords={compliant mechanisms, robotic fingers, tendon-driven mechanisms},\n  publisher={Frontiers},\n  url_pdf  = {https://www.frontiersin.org/articles/10.3389/frobt.2019.00047/full},\n  url_video = {https://youtu.be/Efc_zdzZyZ8},\n  doi = {10.3389/frobt.2019.00047}\n}\n\n

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\n \n\n \n \n Kontoudis, G. P; Liarokapis, M.; and Vamvoudakis, K. G\n\n\n \n \n \n \n \n A Compliant, Underactuated Finger for Anthropomorphic Hands.\n \n \n \n \n\n\n \n\n\n\n In IEEE/RAS-EMBS International Conference on Rehabilitation Robotics (ICORR), pages 682–688, June 2019. \n \n\n\n\n
\n\n\n\n \n \n $\"A$ paper\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{Kontoudis2019ICORR,\n  title={A Compliant, Underactuated Finger for Anthropomorphic Hands},\n  abstract = {This paper presents a compliant, underactuated finger for the development of anthropomorphic robotic and prosthetic hands. The finger achieves both flexion/extension and adduction/abduction on the metacarpophalangeal joint, by using two actuators. The design employs moment arm pulleys to drive the tendon laterally and amplify the abduction motion, while also maintaining the flexion motion. Particular emphasis has been given to the analysis of the mechanism. The proposed finger has been fabricated with the hybrid deposition manufacturing technique and the actuation mechanism's efficiency has been validated with experiments that include the computation of the reachable workspace, the assessment of the exerted forces at the fingertip, the demonstration of the feasible motions, and the presentation of the grasping and manipulation capabilities. The proposed mechanism facilitates the collaboration of the two actuators to increase the exerted finger forces. Moreover, the extended workspace allows the execution of dexterous manipulation tasks.},\n  author={Kontoudis, George P and Liarokapis, Minas and Vamvoudakis, Kyriakos G},\n  booktitle={IEEE/RAS-EMBS International Conference on Rehabilitation Robotics (ICORR)},\n  pages={682--688},\n  month = {June},\n  year={2019},\n  keywords={compliant mechanisms, robotic fingers, tendon-driven mechanisms},\n  url_paper =    {ICORR19_Kontoudis_CompliantUnderactuatedFinger.pdf},\n  doi = {10.1109/ICORR.2019.8779435}\n}\n\n\n

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\n \n 2018\n \n \n (1)\n \n \n

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\n \n\n \n \n Kontoudis, G. P\n\n\n \n \n \n \n \n Adaptive, anthropomorphic, robot hands for grasping and in-hand manipualtion.\n \n \n \n \n\n\n \n\n\n\n thesis, Virginia Tech, 2018.\n \n\n\n\n
\n\n\n\n \n \n $\"Adaptive,$ pdf\n \n \n \n $\"Adaptive,$ html\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@thesis{Kontoudis2018Adaptive,\n    author    = {Kontoudis, George P},\n    title     = {Adaptive, anthropomorphic, robot hands for grasping and in-hand manipualtion},\n    school    = {Virginia Tech},\n    year      = {2018},\n    keywords={compliant mechanisms, differential mechanisms, robotic fingers, robot hands, tendon-driven mechanisms},\n    url_pdf  = {Kontoudis_GP_T_2018.pdf},\n    url_html = {https://vtechworks.lib.vt.edu/handle/10919/87404}\n}\n\n

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\n \n 2017\n \n \n (1)\n \n \n

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\n \n\n \n \n Tsoukalas, K. D; Kontoudis, G. P; and Vamvoudakis, K. G\n\n\n \n \n \n \n Active-Bayesian learning for cooperation connectivity in dynamic cyber-physical-human systems.\n \n \n \n\n\n \n\n\n\n In IEEE Symposium Series on Computational Intelligence (SSCI), pages 1–7, 2017. \n [Invited submission]\n\n\n\n
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@inproceedings{Tsoukalas2017ADPRL,\n  title={Active-{B}ayesian learning for cooperation connectivity in dynamic cyber-physical-human systems},\n  abstract = {This work presents a novel learning framework for instrumental utility of behavioral connectivity within dynamic social networks. The combination of active and Bayesian learning techniques enables the proposed algorithm to learn how to predict a likelihood of group-stages of cooperation, according to observable behavioral connectivity within a dynamic social network. A labeler of social network data defines the group-stages of cooperation, and classifies observed behavioral connectivity according to them, in order to populate a training data pool for the proposed learning algorithm. Moreover, a multiple ordinary least squares (OLS) regression, is used to estimate the behavioral intention of social actors, in order to automate interventions for the improvement of actors' behavioral utility within the dynamic social network. We illustrate our framework through simulation examples that showcase the efficiency of the proposed algorithm to accurately predict predefined group-stages of cooperation in human-driven environments.},\n  author={Tsoukalas, Kyriakos D and Kontoudis, George P  and Vamvoudakis, Kyriakos G},\n  booktitle={IEEE Symposium Series on Computational Intelligence (SSCI)},\n  pages={1--7},\n  year={2017},\n  note = {[Invited submission]},\n  keywords={bayesian inference, social networks},\n  doi = {10.1109/SSCI.2017.8280941}\n}\n\n

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\n \n 2016\n \n \n (1)\n \n \n

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\n \n\n \n \n Kontoudis, G. P\n\n\n \n \n \n \n \n Design and development of an underactuted, anthropomorphic robot hand.\n \n \n \n \n\n\n \n\n\n\n thesis, National Technical University of Athens, 2016.\n In Greek\n\n\n\n
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@thesis{Kontoudis2016Design,\n    author    = {Kontoudis, George P},\n    title     = {Design and development of an underactuted, anthropomorphic robot hand},\n    school    = {National Technical University of Athens},\n    year      = {2016},\n    note = {In Greek},\n    keywords={compliant mechanisms, differential mechanisms, prosthetics, tendon-driven mechanisms},\n    url_pdf  = {KontoudisThesis.pdf}\n}\n\n

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\n \n 2015\n \n \n (3)\n \n \n

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\n \n\n \n \n Kontoudis, G. P; Liarokapis, M. V; Zisimatos, A. G; Mavrogiannis, C. I; and Kyriakopoulos, K. J\n\n\n \n \n \n \n \n Open-source, anthropomorphic, underactuated robot hands with a selectively lockable differential mechanism: Towards affordable prostheses.\n \n \n \n \n\n\n \n\n\n\n In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 5857–5862, 2015. \n \n\n\n\n
\n\n\n\n \n \n $\"Open-source,$ pdf\n \n \n \n $\"Open-source,$ video\n \n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n\n\n\n

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@inproceedings{Kontoudis2015IROS,\n  title={Open-source, anthropomorphic, underactuated robot hands with a selectively lockable differential mechanism: Towards affordable prostheses},\n  abstract = {In this paper we present an open-source design for the development of low-complexity, anthropomorphic, underactuated robot hands with a selectively lockable differential mechanism. The differential mechanism used is a variation of the whiffletree (or seesaw) mechanism, which introduces a set of locking buttons that can block the motion of each finger. The proposed design is unique since with a single motor and the proposed differential mechanism the user is able to control each finger independently and switch between different grasping postures in an intuitive manner. Anthropomorphism of robot structure and motion is achieved by employing in the design process an index of anthropomorphism. The proposed robot hands can be easily fabricated using low-cost, off-the-shelf materials and rapid prototyping techniques. The efficacy of the proposed design is validated through different experimental paradigms involving grasping of everyday life objects and execution of daily life activities. The proposed hands can be used as affordable prostheses, helping amputees regain their lost dexterity.},\n  author={Kontoudis, George P and Liarokapis, Minas V and Zisimatos, Agisilaos G and Mavrogiannis, Christoforos I and Kyriakopoulos, Kostas J},\n  booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},\n  pages={5857--5862},\n  year={2015},\n  keywords={compliant mechanisms, differential mechanisms, prosthetics, tendon-driven mechanisms},\n  url_pdf =    {IROS2015_Kontoudis_AffordableProstheses.pdf},\n  url_video =   {https://www.youtube.com/watch?v=LoG_JTOIMO4},\n  doi = {10.1109/IROS.2015.7354209}\n}\n\n

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\n \n\n \n \n Kontoudis, G. P; Liarokapis, M. V; Zisimatos, A. G; Mavrogiannis, C. I; and Kyriakopoulos, K. J\n\n\n \n \n \n \n \n How to Create Affordable, Anthropomorphic, Personalized, Light-Weight Prosthetic Hands.\n \n \n \n \n\n\n \n\n\n\n Technical Report National Technical University of Athens, 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"How$ pdf\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@techreport{Kontoudis2015TR,\n  title={How to Create Affordable, Anthropomorphic, Personalized, Light-Weight Prosthetic Hands},\n  author={Kontoudis, George P and Liarokapis, Minas V and Zisimatos, Agisilaos G and Mavrogiannis, Christoforos I and Kyriakopoulos, Kostas J},\n  year={2015},\n  institution={National Technical University of Athens},\n  url_pdf =    {TR2015_OpenBionics_ProstheticHandsGuide.pdf}\n}\n\n

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\n \n\n \n \n Zisimatos, A. G; Liarokapis, M. V; Mavrogiannis, C. I; Kontoudis, G. P; and Kyriakopoulos, K. J\n\n\n \n \n \n \n \n How to Create Affordable, Modular, Light-Weight, Underactuated, Compliant Robot Hands.\n \n \n \n \n\n\n \n\n\n\n Technical Report National Technical University of Athens, 2015.\n \n\n\n\n
\n\n\n\n \n \n $\"How$ pdf\n \n \n \n $\"How$ video\n \n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n

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@techreport{Zisimatos2015TR,\n  title={How to Create Affordable, Modular, Light-Weight, Underactuated, Compliant Robot Hands},\n  author={Zisimatos, Agisilaos G and Liarokapis, Minas V and Mavrogiannis, Christoforos I and Kontoudis, George P and Kyriakopoulos, Kostas J},\n  year={2015},\n  institution={National Technical University of Athens},\n  url_pdf =    {TR2015_OpenBionics_RobotHandsGuide.pdf},\n  url_video =   {https://youtu.be/Z8mkwvJW-Xs}\n}

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