text:
@inproceedings{oguz_haptic_2010,
	address = {Waltham, MA, USA},
	title = {Haptic negotiation and role exchange for collaboration in virtual environments},
	copyright = {All rights reserved},
	isbn = {978-1-4244-6821-8},
	url = {http://ieeexplore.ieee.org/document/5444628/},
	doi = {10.1109/HAPTIC.2010.5444628},
	abstract = {We investigate how collaborative guidance can be realized in multimodal virtual environments for dynamic tasks involving motor control. Haptic guidance in our context can be deﬁned as any form of force/tactile feedback that the computer generates to help a user execute a task in a faster, more accurate, and subjectively more pleasing fashion. In particular, we are interested in determining guidance mechanisms that best facilitate task performance and arouse a natural sense of collaboration. We suggest that a haptic guidance system can be further improved if it is supplemented with a role exchange mechanism, which allows the computer to adjust the forces it applies to the user in response to his/her actions. Recent work on collaboration and role exchange presented new perspectives on deﬁning roles and interaction. However existing approaches mainly focus on relatively basic environments where the state of the system can be deﬁned with a few parameters. We designed and implemented a complex and highly dynamic multimodal game for testing our interaction model. Since the state space of our application is complex, role exchange needs to be implemented carefully. We deﬁned a novel negotiation process, which facilitates dynamic communication between the user and the computer, and realizes the exchange of roles using a three-state ﬁnite state machine. Our preliminary results indicate that even though the negotiation and role exchange mechanism we adopted does not improve performance in every evaluation criteria, it introduces a more personal and humanlike interaction model.},
	language = {en},
	urldate = {2020-05-25},
	booktitle = {2010 {IEEE} {Haptics} {Symposium}},
	publisher = {IEEE},
	author = {Oguz, S. Ozgur and Kucukyilmaz, Ayse and Sezgin, Tevfik Metin and Basdogan, Cagatay},
	month = mar,
	year = {2010},
	pages = {371--378},
	file = {Oguz et al. - 2010 - Haptic negotiation and role exchange for collabora.pdf:/home/oz/Zotero/storage/DQFFLP3P/Oguz et al. - 2010 - Haptic negotiation and role exchange for collabora.pdf:application/pdf}
}

@inproceedings{gabler_game-theoretic_2017,
	address = {Singapore, Singapore},
	title = {A game-theoretic approach for adaptive action selection in close proximity human-robot-collaboration},
	copyright = {All rights reserved},
	isbn = {978-1-5090-4633-1},
	url = {http://ieeexplore.ieee.org/document/7989336/},
	doi = {10.1109/ICRA.2017.7989336},
	abstract = {With the integration of Human-Robot Collaboration (HRC) in industrial assembly scenarios, robot systems face numerous challenges. In contrast to classic robot systems which follow a pre-programmed and ﬁxed sequence of actions, an interaction scenario with humans in the loop requires mutual adaptation. In this paper a framework based on game theory is presented that allows robots to choose appropriate actions with respect to the action of human coworkers when collaborating in close proximity. The proposed framework models HRC scenarios as iterative games and selects action-strategies for the Human-Robot Team (HRT) by ﬁnding the Nash-Equilibria (NEs) of these games. In contrast to most common approaches, our proposed HRC-game treats the decision-making behavior equally for all agents involved. Therefore, the concept of game theory is applied to evaluate the mutual interference of all actions on the HRT to obtain pareto-optimal NEs, i.e. team-optimal action-allocations. The general framework of the proposed HRC-game is realized on an interactive pick-andplace scenario in close proximity. This exemplary HRC-game is tested in a human subject experiment of a KUKA LWR 4+ robot and a human coworker assembling toy-bricks in close proximity. The experimental measurements and statistically signiﬁcant improvements in the subjective feedback hold as a proof-of-concept of the proposed HRC-game model.},
	language = {en},
	urldate = {2020-05-25},
	booktitle = {2017 {IEEE} {International} {Conference} on {Robotics} and {Automation} ({ICRA})},
	publisher = {IEEE},
	author = {Gabler, Volker and Stahl, Tim and Huber, Gerold and Oguz, Ozgur and Wollherr, Dirk},
	month = may,
	year = {2017},
	keywords = {myself},
	pages = {2897--2903},
	file = {Gabler et al. - 2017 - A game-theoretic approach for adaptive action sele.pdf:/home/oz/Zotero/storage/52UDNZ9N/Gabler et al. - 2017 - A game-theoretic approach for adaptive action sele.pdf:application/pdf}
}

@article{oguz_supporting_2012,
	title = {Supporting {Negotiation} {Behavior} with {Haptics}-{Enabled} {Human}-{Computer} {Interfaces}},
	volume = {5},
	copyright = {All rights reserved},
	issn = {1939-1412},
	url = {http://ieeexplore.ieee.org/document/6231631/},
	doi = {10.1109/TOH.2012.37},
	abstract = {An active research goal for human-computer interaction is to allow humans to communicate with computers in an intuitive and natural fashion, especially in real-life interaction scenarios. One approach that has been advocated to achieve this has been to build computer systems with human-like qualities and capabilities. In this paper, we present insight on how human-computer interaction can be enriched by employing the computers with behavioral patterns that naturally appear in human-human negotiation scenarios. For this purpose, we introduce a two-party negotiation game specifically built for studying the effectiveness of haptic and audio-visual cues in conveying negotiation related behaviors. The game is centered around a real-time continuous two-party negotiation scenario based on the existing game-theory and negotiation literature. During the game, humans are confronted with a computer opponent, which can display different behaviors, such as concession, competition, and negotiation. Through a user study, we show that the behaviors that are associated with human negotiation can be incorporated into human-computer interaction, and the addition of haptic cues provides a statistically significant increase in the human-recognition accuracy of machine-displayed behaviors. In addition to aspects of conveying these negotiation-related behaviors, we also focus on and report game-theoretical aspects of the overall interaction experience. In particular, we show that, as reported in the game-theory literature, certain negotiation strategies such as tit-for-tat may generate maximum combined utility for the negotiating parties, providing an excellent balance between the energy spent by the user and the combined utility of the negotiating parties.},
	language = {en},
	number = {3},
	urldate = {2020-05-25},
	journal = {IEEE Trans. Haptics},
	author = {Oguz, S. Ozgur and Kucukyilmaz, Ayse and Metin Sezgin, Tevfik and Basdogan, Cagatay},
	year = {2012},
	keywords = {myself},
	pages = {274--284},
	file = {Oguz et al. - 2012 - Supporting Negotiation Behavior with Haptics-Enabl.pdf:/home/oz/Zotero/storage/DPF4XR8G/Oguz et al. - 2012 - Supporting Negotiation Behavior with Haptics-Enabl.pdf:application/pdf}
}

@incollection{peer_improving_2012,
	address = {London},
	title = {Improving {Human}-{Computer} {Cooperation} {Through} {Haptic} {Role} {Exchange} and {Negotiation}},
	copyright = {All rights reserved},
	isbn = {978-1-4471-2753-6 978-1-4471-2754-3},
	url = {http://link.springer.com/10.1007/978-1-4471-2754-3_13},
	language = {en},
	urldate = {2020-05-25},
	booktitle = {Immersive {Multimodal} {Interactive} {Presence}},
	publisher = {Springer London},
	author = {Kucukyilmaz, Ayse and Oguz, Salih Ozgur and Sezgin, Tevfik Metin and Basdogan, Cagatay},
	editor = {Peer, Angelika and Giachritsis, Christos D.},
	year = {2012},
	doi = {10.1007/978-1-4471-2754-3_13},
	note = {Series Title: Springer Series on Touch and Haptic Systems},
	keywords = {myself},
	pages = {229--254},
	file = {Kucukyilmaz et al. - 2012 - Improving Human-Computer Cooperation Through Hapti.pdf:/home/oz/Zotero/storage/ZAFFHG4Z/Kucukyilmaz et al. - 2012 - Improving Human-Computer Cooperation Through Hapti.pdf:application/pdf}
}

@inproceedings{dinh_approach_2015,
	address = {Lyon},
	title = {An approach to integrate human motion prediction into local obstacle avoidance in close human-robot collaboration},
	copyright = {All rights reserved},
	isbn = {978-1-4673-8029-4},
	url = {http://ieeexplore.ieee.org/document/7428221/},
	doi = {10.1109/ARSO.2015.7428221},
	abstract = {Within Human-Robot Collaboration (HRC) safety is one key-issue that has to be guaranteed at any time during joint collaboration. Collisions in a shared workspace of a Human-Robot-Team (HRT) must be prevented. In addition, the comfort of the collaboration behavior should be provided. Facing these challenges, a robot has to be able to detect critical states at an early stage on the one hand and should react to them within a very short time span on the other hand. In this paper a collision avoidance algorithm using compliance control that guarantees a fast reaction to dynamic obstacles, e.g. humans, without the need of high computational effort is outlined. To further improve the avoidance behavior of the robot, a human motion prediction algorithm based on the minimum-jerk model is integrated. In an experimental analysis of a case-study about collecting LEGO-bricks on a table with various subjects, the impact of the integration of human motion prediction on both the robot’s reaction time and human’s perception of the robot co-worker is studied. Finally, the comfort and acceptance of the robot colleague by the human collaborator is drawn out through an analysis of the subjective human feedback questionnaires.},
	language = {en},
	urldate = {2020-05-25},
	booktitle = {2015 {IEEE} {International} {Workshop} on {Advanced} {Robotics} and its {Social} {Impacts} ({ARSO})},
	publisher = {IEEE},
	author = {Dinh, Khoi Hoang and Oguz, Ozgur and Huber, Gerold and Gabler, Volker and Wollherr, Dirk},
	month = jun,
	year = {2015},
	keywords = {myself},
	pages = {1--6},
	file = {Dinh et al. - 2015 - An approach to integrate human motion prediction i.pdf:/home/oz/Zotero/storage/KRYGKXDJ/Dinh et al. - 2015 - An approach to integrate human motion prediction i.pdf:application/pdf}
}

@article{oguz_inverse_2018,
	title = {An {Inverse} {Optimal} {Control} {Approach} to {Explain} {Human} {Arm} {Reaching} {Control} {Based} on {Multiple} {Internal} {Models}},
	volume = {8},
	copyright = {All rights reserved},
	issn = {2045-2322},
	url = {http://www.nature.com/articles/s41598-018-23792-7},
	doi = {10.1038/s41598-018-23792-7},
	language = {en},
	number = {1},
	urldate = {2020-05-25},
	journal = {Sci Rep},
	author = {Oguz, Ozgur S. and Zhou, Zhehua and Glasauer, Stefan and Wollherr, Dirk},
	month = dec,
	year = {2018},
	keywords = {myself},
	pages = {5583},
	file = {Oguz et al. - 2018 - An Inverse Optimal Control Approach to Explain Hum.pdf:/home/oz/Zotero/storage/REQLVEAW/Oguz et al. - 2018 - An Inverse Optimal Control Approach to Explain Hum.pdf:application/pdf}
}

@inproceedings{oguz_progressive_2017,
	title = {Progressive stochastic motion planning for human-robot interaction},
	copyright = {All rights reserved},
	doi = {10.1109/ROMAN.2017.8172456},
	abstract = {This paper introduces a new approach to optimal online motion planning for human-robot interaction scenarios. For a safe, comfortable, and efficient interaction between human and robot working in close proximity, robot motion has to be agile and perceived as natural by the human partner. The robot has to be aware of its environment, including human motions, in order to proactively take actions while ensuring safety, and task fulfillment. Human motion prediction constitutes the fundamental perception input for the motion planner. The prediction system, which is based on probabilistic movement primitives, generates a prediction of human motion as a trajectory distribution learned in an offline phase. The proposed stochastic optimization-based planning algorithm then progressively finds feasible optimization parameters to replan the motion online that ensures collision avoidance while minimizing the task-related trajectory cost. Our simulation results show that the proposed approach produces collision-free trajectories while still reaching the goal successfully. We also highlight the performance of our planner in comparison to previous methods in stochastic motion planning.},
	booktitle = {2017 26th {IEEE} {International} {Symposium} on {Robot} and {Human} {Interactive} {Communication} ({RO}-{MAN})},
	author = {Oguz, Ozgur S. and Sari, Omer C. and Dinh, Khoi H. and Wollherr, Dirk},
	month = aug,
	year = {2017},
	note = {ISSN: 1944-9437},
	keywords = {myself},
	pages = {1194--1201},
	file = {Oguz_et_al_2017_Progressive_stochastic_motion_planning_for_human-robot_interaction.pdf:/home/oz/gdrive/zotero/Oguz_et_al_2017_Progressive_stochastic_motion_planning_for_human-robot_interaction.pdf:application/pdf}
}

@article{oguz_hybrid_2018,
	title = {A {Hybrid} {Framework} for {Understanding} and {Predicting} {Human} {Reaching} {Motions}},
	volume = {5},
	copyright = {All rights reserved},
	issn = {2296-9144},
	url = {https://www.frontiersin.org/articles/10.3389/frobt.2018.00027/full},
	doi = {10.3389/frobt.2018.00027},
	abstract = {Robots collaborating naturally with a human partner in a confined workspace need to understand and predict human motions. For understanding, a model-based approach is required as the human motor control system relies on the biomechanical properties to control and execute actions. The model-based control models explain human motions descriptively, which in turn enables predicting and analyzing human movement behaviors. In motor control, reaching motions are framed as an optimization problem. However, different optimality criteria predict disparate motion behavior. Therefore, the inverse problem -- finding the optimality criterion from a given arm motion trajectory -- is not unique. This paper implements an inverse optimal control (IOC) approach to determine the combination of cost functions that governs a motion execution. The results indicate that reaching motions depend on a trade-off between kinematics and dynamics related cost functions. However, the computational efficiency is not sufficient for online prediction to be utilized for HRI. In order to predict human reaching motions with high efficiency and accuracy, we combine the IOC approach with a probabilistic movement primitives formulation. This hybrid model allows an online-capable prediction while taking into account motor variability, and the interpersonal differences. The proposed framework affords a descriptive and a generative model of human reaching motions which can be effectively utilized online for human-in-the-loop robot control and task execution.},
	language = {English},
	urldate = {2020-05-25},
	journal = {Front. Robot. AI},
	author = {Oguz, Ozgur S. and Zhou, Zhehua and Wollherr, Dirk},
	year = {2018},
	note = {Publisher: Frontiers},
	keywords = {myself},
	file = {Oguz_et_al_2018_A_Hybrid_Framework_for_Understanding_and_Predicting_Human_Reaching_Motions.pdf:/home/oz/gdrive/zotero/Oguz_et_al_2018_A_Hybrid_Framework_for_Understanding_and_Predicting_Human_Reaching_Motions.pdf:application/pdf}
}

@article{driess_deep_2020,
	title = {Deep {Visual} {Heuristics}: {Learning} {Feasibility} of {Mixed}-{Integer} {Programs} for {Manipulation} {Planning}},
	copyright = {All rights reserved},
	abstract = {In this paper, we propose a deep neural network that predicts the feasibility of a mixed-integer program from visual input for robot manipulation planning. Integrating learning into task and motion planning is challenging, since it is unclear how the scene and goals can be encoded as input to the learning algorithm in a way that enables to generalize over a variety of tasks in environments with changing numbers of objects and goals. To achieve this, we propose to encode the scene and the target object directly in the image space.},
	language = {en},
	author = {Driess, Danny and Oguz, Ozgur and Ha, Jung-Su and Toussaint, Marc},
	year = {2020},
	keywords = {myself},
	pages = {8},
	file = {Driess et al. - Deep Visual Heuristics Learning Feasibility of Mi.pdf:/home/oz/Zotero/storage/39XQTRFR/Driess et al. - Deep Visual Heuristics Learning Feasibility of Mi.pdf:application/pdf}
}

@article{driess_hierarchical_2019,
	title = {Hierarchical {Task} and {Motion} {Planning} using {Logic}-{Geometric} {Programming} ({HLGP})},
	copyright = {All rights reserved},
	abstract = {In this work, we present a hierarchical approach to task and motion planning (TAMP) within an optimizationbased framework. Recent work on formulating TAMP as a logic enhanced nonlinear program has shown remarkable capabilities. However, scaling this approach to domains with many discrete decisions or longer horizons implies a computational bottleneck. To overcome this, we introduce hierarchies within this framework, where on coarser levels a problem with less discrete decisions is solved. Formally, the hierarchies are deﬁned in a way that the resulting nonlinear programs on coarser hierarchy levels are lower bounds on the ﬁner hierarchies. We demonstrate the generality of the approach for both a bi-manual manipulation task and a mobile manipulation scenario which includes a “worm” like walking robot.},
	language = {en},
	author = {Driess, Danny and Oguz, Ozgur S and Toussaint, Marc},
	year = {2019},
	keywords = {myself},
	pages = {2},
	file = {Driess et al. - Hierarchical Task and Motion Planning using Logic-.pdf:/home/oz/Zotero/storage/LS59UECK/Driess et al. - Hierarchical Task and Motion Planning using Logic-.pdf:application/pdf}
}

@incollection{kulic_hybrid_2017,
	address = {Cham},
	title = {Hybrid {Human} {Motion} {Prediction} for {Action} {Selection} {Within} {Human}-{Robot} {Collaboration}},
	volume = {1},
	copyright = {All rights reserved},
	isbn = {978-3-319-50114-7 978-3-319-50115-4},
	url = {http://link.springer.com/10.1007/978-3-319-50115-4_26},
	abstract = {We present a Human-Robot-Collaboration (HRC) framework consisting of a hybrid human motion prediction approach together with a game theoretical action selection. In essence, the robot is required to predict the motions of the human co-worker, and to proactively decide on its actions. For our prediction framework, model-based human motion trajectories are learned by data-driven methods for eﬃcient trajectory rollouts in which obstacles are also considered. We provide the reliability analysis of human trajectory predictions within a human-robot collaboration experimental setup. The HRC scenario is modeled as an iterative game to select the actions for the Human-Robot-Team (HRT) by ﬁnding the Nash Equilibrium of the game. Experimental evaluation shows how the proposed prediction approach can be successfully integrated into a game theory based action selection framework.},
	language = {en},
	urldate = {2020-05-25},
	booktitle = {2016 {International} {Symposium} on {Experimental} {Robotics}},
	publisher = {Springer International Publishing},
	author = {Oguz, Ozgur S. and Gabler, Volker and Huber, Gerold and Zhou, Zhehua and Wollherr, Dirk},
	editor = {Kulić, Dana and Nakamura, Yoshihiko and Khatib, Oussama and Venture, Gentiane},
	year = {2017},
	doi = {10.1007/978-3-319-50115-4_26},
	note = {Series Title: Springer Proceedings in Advanced Robotics},
	pages = {289--298},
	file = {Oguz et al. - 2017 - Hybrid Human Motion Prediction for Action Selectio.pdf:/home/oz/Zotero/storage/FV6U43FC/Oguz et al. - 2017 - Hybrid Human Motion Prediction for Action Selectio.pdf:application/pdf}
}

@article{hoang_dinh_adaptation_2019,
	title = {Adaptation and {Transfer} of {Robot} {Motion} {Policies} for {Close} {Proximity} {Human}-{Robot} {Interaction}},
	volume = {6},
	copyright = {All rights reserved},
	issn = {2296-9144},
	url = {https://www.frontiersin.org/articles/10.3389/frobt.2019.00069/full},
	doi = {10.3389/frobt.2019.00069},
	abstract = {In the context of human-robot collaboration in close proximity, safety and comfort are the two important aspects to achieve joint tasks efficiently. For safety, the robot must be able to avoid dynamic obstacles such as a human arm with high reliability. For comfort, the trajectories and avoidance behavior of the robot need to be predictable to the humans. Moreover, these two aspects might be different from person to person or from one task to another. This work presents a framework to generate predictable motions with dynamic obstacle avoidance for the robot interacting with the human by using policy improvement method. The trajectories are generated using Dynamic Motion Primitives with an additional potential field term that penalizes trajectories that may lead to collisions with obstacles. Furthermore, human movements are predicted using a data-driven approach for proactive avoidance. A cost function is defined which measures different aspects that affect the comfort and predictability of human co-workers (e.g. human response time, joint jerk). This cost function is then minimized during human-robot interaction by the means of policy improvement through black-box optimization to generate robot trajectories that adapt to human preferences and avoid obstacles. User studies are performed to evaluate the trust and comfort of human co-workers when working with the robot. In addition, the studies are also extended to various scenarios and different users to analyze the task transferability. This improves the learning performance when switching to a new task or the robot has to adapt to a different co-worker.},
	language = {English},
	urldate = {2020-05-25},
	journal = {Front. Robot. AI},
	author = {Hoang Dinh, Khoi and Oguz, Ozgur S. and Elsayed, Mariam and Wollherr, Dirk},
	year = {2019},
	note = {Publisher: Frontiers},
	keywords = {myself},
	file = {Hoang_Dinh_et_al_2019_Adaptation_and_Transfer_of_Robot_Motion_Policies_for_Close_Proximity.pdf:/home/oz/gdrive/zotero/Hoang_Dinh_et_al_2019_Adaptation_and_Transfer_of_Robot_Motion_Policies_for_Close_Proximity.pdf:application/pdf}
}

@article{oguz_learning_2018,
	title = {Learning {Hand} {Movement} {Interaction} {Control} {Using} {RNNs}: {From} {HHI} to {HRI}},
	volume = {3},
	copyright = {All rights reserved},
	issn = {2377-3766},
	shorttitle = {Learning {Hand} {Movement} {Interaction} {Control} {Using} {RNNs}},
	doi = {10.1109/LRA.2018.2862923},
	abstract = {A key problem in robotics is enabling an autonomous agent to perform human-like arm movements in close proximity to another human. However, modeling the human decision and control process of the movement during dyadic interaction presents a challenge. Although, most prior approaches rely on multicomponent robot motion planning architectures, we use data of two humans performing interfering arm reaching movements to extract and transfer interaction behavior control skill to a robotic agent. A recurrent neural network-based framework is constructed to learn a policy that computes control signals for a robot end effector in order to replace one human. The learned policy is benchmarked against unseen interaction data and a state-of-the-art learning from demonstration framework in simulated scenarios. We compare several architectures and investigate a new activation function of three stacked tanh(). The results show that the proposed framework successfully learns a policy to imitate human movement behavior control during dyadic interaction. The policy is transferred to a real robot and its feasibility for close-proximity human-robot interaction is shown.},
	number = {4},
	journal = {IEEE Robotics and Automation Letters},
	author = {Oguz, Ozgur S. and Pfirrmann, Ben M. and Guo, Mingpan and Wollherr, Dirk},
	month = oct,
	year = {2018},
	note = {Conference Name: IEEE Robotics and Automation Letters},
	keywords = {myself},
	pages = {4100--4107},
	url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8424874},
	file = {Oguz_et_al_2018_Learning_Hand_Movement_Interaction_Control_Using_RNNs_-_From_HHI_to_HRI.pdf:/home/oz/gdrive/zotero/Oguz_et_al_2018_Learning_Hand_Movement_Interaction_Control_Using_RNNs_-_From_HHI_to_HRI.pdf:application/pdf}
}

@article{hartmann_robust_2020,
	title = {Robust {Task} and {Motion} {Planning} for {Long}-{Horizon} {Architectural} {Construction} {Planning}},
	copyright = {All rights reserved},
	url = {http://arxiv.org/abs/2003.07754},
	abstract = {Integrating robotic systems in architectural and construction processes is of core interest to increase the efficiency of the building industry. Automated planning for such systems enables design analysis tools and facilitates faster design iteration cycles for designers and engineers. However, generic task-and-motion planning (TAMP) for long-horizon construction processes is beyond the capabilities of current approaches. In this paper, we develop a multi-agent TAMP framework for long horizon problems such as constructing a full-scale building. To this end we extend the Logic-Geometric Programming framework by sampling-based motion planning,a limited horizon approach, and a task-specific structural stability optimization that allow an effective decomposition of the task. We show that our framework is capable of constructing a large pavilion built from several hundred geometrically unique building elements from start to end autonomously.},
	urldate = {2020-05-25},
	journal = {arXiv:2003.07754 [cs]},
	author = {Hartmann, Valentin N. and Oguz, Ozgur S. and Driess, Danny and Toussaint, Marc and Menges, Achim},
	month = mar,
	year = {2020},
	note = {arXiv: 2003.07754},
	keywords = {myself},
	file = {Hartmann_et_al_2020_Robust_Task_and_Motion_Planning_for_Long-Horizon_Architectural_Construction.pdf:/home/oz/gdrive/zotero/Hartmann_et_al_2020_Robust_Task_and_Motion_Planning_for_Long-Horizon_Architectural_Construction.pdf:application/pdf}
}

@article{oguz_ontology_2019,
	title = {An {Ontology} for {Human}-{Human} {Interactions} and {Learning} {Interaction} {Behavior} {Policies}},
	volume = {8},
	copyright = {All rights reserved},
	issn = {2573-9522, 2573-9522},
	url = {https://dl.acm.org/doi/10.1145/3326539},
	doi = {10.1145/3326539},
	language = {en},
	number = {3},
	urldate = {2020-05-25},
	journal = {J. Hum.-Robot Interact.},
	author = {Oguz, Ozgur S. and Rampeltshammer, Wolfgang and Paillan, Sebastian and Wollherr, Dirk},
	month = aug,
	year = {2019},
	keywords = {myself},
	pages = {1--26},
	file = {Oguz et al. - 2019 - An Ontology for Human-Human Interactions and Learn.pdf:/home/oz/Zotero/storage/FCWMD7A6/Oguz et al. - 2019 - An Ontology for Human-Human Interactions and Learn.pdf:application/pdf}
}

@inproceedings{oguz_ws_learning_2018,
	title = {Learning {Hand} {Movement} {Interaction} {Control} {Using} {RNNs}: {From} {HHI} to {HRI}},
	author = {Oguz, Ozgur S. and Pfirrmann, Ben M. and Wollherr, Dirk},
	copyright = {All rights reserved},
	booktitle = {{NeurIPS} {Imitation} {Learning} and its {Challenges} in {Robotics} {Workshop}},
	year = {2018},
	file = {2018_Learning_Hand_Movement_Interaction_Control_Using_RNNs_-_From_HHI_to_HRI.pdf:/home/oz/gdrive/zotero/2018_Learning_Hand_Movement_Interaction_Control_Using_RNNs_-_From_HHI_to_HRI.pdf:application/pdf}
}

@inproceedings{axenopulos_hybrid_2019,
	address = {Leicester, United Kingdom},
	title = {A {Hybrid} {Human}-{Robot} {Collaborative} {Environment} for {Recycling} {Electrical} and {Electronic} {Equipment}},
	copyright = {All rights reserved},
	isbn = {978-1-72814-034-6},
	url = {https://ieeexplore.ieee.org/document/9060304/},
	doi = {10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00312},
	abstract = {In this paper, a novel hybrid human-robot recycling plant for electrical and electronic equipment is introduced. The proposed system will be developed in the context of the European research project HR-Recycler and its goal is to offer a hybrid collaboration environment for humans and robots. Through this collaboration, several currently manual, expensive, hazardous and time-consuming tasks of WEEE materials pre-processing will be substituted by automatic robotic-based procedures (categorization of electric/electronic devices, disassembling them, sorting of device components), before the materials enter the fine shredding machine. Although several solutions have been proposed for automation of recycling other types of waste (e.g. domestic), the industrial application case of WEEE recycling poses significant challenges and it is the first time, to the best of our knowledge, that a hybrid human-robot solution is proposed to address this problem.},
	language = {en},
	urldate = {2020-05-25},
	booktitle = {2019 {IEEE} {SmartWorld}, {Ubiquitous} {Intelligence} \& {Computing}, {Advanced} \& {Trusted} {Computing}, {Scalable} {Computing} \& {Communications}, {Cloud} \& {Big} {Data} {Computing}, {Internet} of {People} and {Smart} {City} {Innovation} ({SmartWorld}/{SCALCOM}/{UIC}/{ATC}/{CBDCom}/{IOP}/{SCI})},
	publisher = {IEEE},
	author = {Axenopulos, Apostolos and Papadopoulos, Georgios and Giakoumis, Dimitrios and Kostavelis, Ioannis and Papadimitriou, Alexis Papadimitriou and Sillaurren, Sara and Bastida, Leire and Oguz, Ozgur and Wollherr, Dirk and Garnica, Eugenio and Vouloutsi, Vasiliki and Verschure, Paul and Tzovaras, Dimitrios and Daras, Petros},
	month = aug,
	year = {2019},
	keywords = {myself},
	pages = {1754--1759},
	file = {Axenopulos et al. - 2019 - A Hybrid Human-Robot Collaborative Environment for.pdf:/home/oz/Zotero/storage/D86AELFE/Axenopulos et al. - 2019 - A Hybrid Human-Robot Collaborative Environment for.pdf:application/pdf}
}

@article{zhou_general_2020,
	title = {A {General} {Framework} to {Increase} {Safety} of {Learning} {Algorithms} for {Dynamical} {Systems} {Based} on {Region} of {Attraction} {Estimation}},
	copyright = {All rights reserved},
	issn = {1941-0468},
	doi = {10.1109/TRO.2020.2992981},
	abstract = {Although the state-of-the-art learning approaches exhibit impressive results for dynamical systems, only a few applications on real physical systems have been presented. One major impediment is that the intermediate policy during the training procedure may result in behaviors that are not only harmful to the system itself but also to the environment. In essence, imposing safety guarantees for learning algorithms is vital for autonomous systems acting in the real world. In this article, we propose a computationally effective and general safe learning framework, specifically for complex dynamical systems. With a proper definition of the safe region, a supervisory control strategy, which switches the actions applied on the system between the learning-based controller and a predefined corrective controller, is given. A simplified system facilitates the estimation of the safe region for the high-dimensional dynamical system. During the learning phase, the belief of the safe region is updated with the actual execution results of the corrective controller, which in turn enables the learning-based controller to have more freedom in choosing its actions. Two examples are given to demonstrate the performance of the proposed framework, one simple inverted pendulum to illustrate the online adaptation method, and one quadcopter control task to show the overall performance.},
	journal = {IEEE Transactions on Robotics},
	author = {Zhou, Zhehua and Oguz, Ozgur S. and Leibold, Marion and Buss, Martin},
	year = {2020},
	note = {Conference Name: IEEE Transactions on Robotics},
	keywords = {myself, safe\_learning},
	pages = {1--19},
	file = {Zhou_et_al_2020_A_General_Framework_to_Increase_Safety_of_Learning_Algorithms_for_Dynamical.pdf:/home/oz/gdrive/zotero/Zhou_et_al_2020_A_General_Framework_to_Increase_Safety_of_Learning_Algorithms_for_Dynamical.pdf:application/pdf}
}

repository: Oz-Oguz/academic-kickstart

repo_name: Oz-Oguz/academic-kickstart


text:
@InProceedings{johanson09a,
  title = {Data Biased Robust Counter Strategies},
  author = {Michael Johanson and Michael Bowling},
  pages = {264--271},
  abstract = {The problem of exploiting information about the environment while still being robust to inaccurate or incomplete information arises in many domains. Competitive imperfect information games where the goal is to maximally exploit an unknown opponent's weaknesses are an example of this problem.  Agents for these games must balance two objectives.  First, they should aim to exploit data from past interactions with the opponent, seeking a best-response counter strategy.  Second, they should aim to minimize losses since the limited data may be misleading or the opponent's strategy may have changed, suggesting an opponent-agnostic Nash equilibrium strategy.  In this paper, we show how to partially satisfy both of these objectives at the same time, producing strategies with favorable tradeoffs between the ability to exploit an opponent and the capacity to be exploited.  Like a recently published technique, our approach involves solving a modified game; however the result is more generally applicable and even performs well in situations with very limited data.  We evaluate our technique in the game of two-player, Limit Texas Hold'em.},
  pdf = {http://jmlr.org/proceedings/papers/v5/johanson09a/johanson09a.pdf},
}

license: MIT

repository: mreid/papersite

license_type: permissive

repo_name: mreid/papersite

branch_name: refs/heads/master

gha_license_id: MIT

gha_language: TeX

extension: bib


text:
%% This BibTeX bibliography file was created using BibDesk.
%% http://bibdesk.sourceforge.net/

%% Created for Spencer Smith at 2015-12-04 11:36:39 -0500

%% Saved with string encoding Unicode (UTF-8)

@inproceedings{Parnas1978,
	Address = {Piscataway, NJ, USA},
	Author = {David L. Parnas},
	Booktitle = {ICSE '78: Proceedings of the 3rd international conference on Software engineering},
	Date-Added = {2015-04-23 14:33:48 +0000},
	Date-Modified = {2015-04-23 14:33:48 +0000},
	Isbn = {none},
	Location = {Atlanta, Georgia, United States},
	Pages = {264--277},
	Publisher = {IEEE Press},
	Title = {Designing Software for Ease of Extension and Contraction},
	Year = {1978}}

@inproceedings{Karchewski2012,
	Address = {Winnipeg, MB, CA},
	Author = {Brandon Karchewski and Peijun Guo and Dieter Stolle},
	Booktitle = {Proceedings of the 65th annual Canadian Geotechnical
	Conference},
	Eventdate = {2012-09-30/2012-10-03},
	Publisher = {Canadian Geotechnical Society},
	Location = {Winnipeg, Manitoba, Canada},
	Title = {Influence of Inherent Anisotropy of Soil Strength on Limit
	Equilibrium Slope Stability Analysis},
	Year = {2012}}

@article{WilsonEtAl2013,
	Author = {Greg Wilson and D.A. Aruliah and C. Titus Brown and Neil P. Chue Hong and Matt Davis and Richard T. Guy and Steven H.D. Haddock and Kathryn D. Huff and Ian M. Mitchell and Mark D. Plumblet and Ben Waugh and Ethan P. White and Paul Wilson},
	Bibsource = {DBLP, http://dblp.uni-trier.de},
	Date-Added = {2015-04-16 14:58:31 +0000},
	Date-Modified = {2015-04-16 14:58:31 +0000},
	Ee = {http://arxiv.org/abs/1210.0530},
	Journal = {CoRR},
	Title = {Best Practices for Scientific Computing},
	Volume = {abs/1210.0530},
	Year = {2013},
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@article{Parnas1972a,
	Author = {David L. Parnas},
	Date-Added = {2015-04-16 14:58:14 +0000},
	Date-Modified = {2015-04-16 14:58:14 +0000},
	Journal = {Comm. ACM, vol. 15, no. 2, pp. 1053-1058},
	Month = {December},
	Title = {On the Criteria To Be Used in Decomposing Systems into Modules},
	Year = {1972},
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@inproceedings{ParnasEtAl1984,
	Address = {Piscataway, NJ, USA},
	Author = {D. L. Parnas and P. C. Clements and D. M. Weiss},
	Booktitle = {ICSE '84: Proceedings of the 7th international conference on Software engineering},
	Date-Added = {2015-04-16 14:54:02 +0000},
	Date-Modified = {2015-04-16 15:01:09 +0000},
	Isbn = {0-8186-0528-6},
	Location = {Orlando, Florida, United States},
	Pages = {408--417},
	Publisher = {IEEE Press},
	Title = {The Modular Structure of Complex Systems},
	Year = {1984}}

@article{ParnasAndClements1986,
	Author = {David L. Parnas and P.C. Clements},
	Date-Added = {2015-03-26 16:00:55 +0000},
	Date-Modified = {2015-03-26 16:00:55 +0000},
	Journal = {IEEE Transactions on Software Engineering},
	Number = {2},
	Pages = {251--257},
	Title = {A Rational Design Process: How and Why to Fake it},
	Volume = {12},
	Year = {February 1986}}

@inproceedings{SmithAndLai2005,
	Address = {Paris, France},
	Author = {W. Spencer Smith and Lei Lai},
	Booktitle = {Proceedings of the First International Workshop on Situational Requirements Engineering Processes -- Methods, Techniques and Tools to Support Situation-Specific Requirements Engineering Processes, SREP'05},
	Date-Added = {2014-10-03 23:54:42 +0000},
	Date-Modified = {2014-10-03 23:54:42 +0000},
	Editor = {J. Ralyt\'{e} and P. \.{A}gerfalk and N. Kraiem},
	Organization = {In conjunction with 13th IEEE International Requirements Engineering Conference},
	Pages = {107--121},
	Title = {A New Requirements Template for Scientific Computing},
	Year = {2005}}

@mastersthesis{Koothoor2013,
	Address = {Hamilton, Ontario, Canada},
	Author = {Nirmitha Koothoor},
	School = {McMaster University},
	Title = {A Document Drive Approach to Certifying Scientific Computing Software},
	Year = {2013}}

@article{ZhuEtAl2005,
	Author = {D.Y. Zhu and C.F. Lee and Q.H. Qian and G.R. Chen},
	Date-Modified = {2015-12-04 16:35:58 +0000},
	Journal = {Can. Geotech. J.},
	Number = {42},
	Pages = {272--278},
	Title = {A concise algorithm for computing the factor of safety using the Morgenstern--Price method},
	Year = {19 February 2005}}

@article{FredlundKrahn,
	Author = {D.G. Fredlund and J.Krahn},
	Journal = {Can. Geotech. J.},
	Number = {14},
	Pages = {429--439},
	Title = {Comparison of slope stability methods of analysis},
	Year = {4 April 1977}}

@article{LiEtAl,
	Author = {Yu-Chao Li and Yun-Min Chen and Tony L.T Zhan and Dao-Sheng Ling and Peter John Cleall},
	Date-Modified = {2015-12-04 16:36:38 +0000},
	Journal = {Can. Geotech. J.},
	Number = {47},
	Pages = {806--820},
	Title = {An efficient approach for locating the critical slip surface in slope stability analyses using a real-coded genetic algorithm},
	Year = {25 June 2010}}

@article{StolleGuo,
	Author = {Dieter Stolle and Peijun Guo},
	Journal = {Can. Geotech. J.},
	Number = {45},
	Pages = {653--662},
	Title = {Limit equilibrum slope stability analysis using rigid finite elements},
	Year = {20 May 2008}}

@misc{UserGuide,
       Author = {Brandon Karchewski},
       Title = {Slope Stability Program (SSP) User's Guide 1.0},
       Year = {8 January 2012}}

@article{Greco1996,
	Author = {Venanzio R. Greco},
	Journal = {J. Geotech. Engrg.},
	Number = {122},
	Pages = {517--525},
	Title = {Efficient monte carlo technique for locating critical slip
	surface},
	Year = {1 July 1996}}

@article{MalkawiEtAl,
	Author = {Abdallah I. Husein Malkawi and Waleed F. Hassan and Sarada K.
	Sarma},
	Journal = {J. Geotech. Geoenviron. Eng.},
	Number = {127},
	Pages = {688--698},
	Title = {Global search method for locating general slip surface using monte
	carlo techniques},
	Year = {1 August 2001}}

@article{ChengEtAl,
	Author = {Y. M. Cheng and Liang Li and Shi-chun Chi and W. B. Wei},
	Journal = {Computers and Geotechnics},
	Number = {24},
	Pages = {92--103},
	Title = {Particle swarm optimization algorithm for the location of the
	critical non-circular failure surface in two-dimensional slope stability
	analysis},
	Year = {27 Decemeber 2006}}

@book{HoffmanAndStrooper1995,
	Address = {New York, NY, USA},
	Author = {Daniel M. Hoffman and Paul A. Strooper},
	Date-Added = {2017-09-07 18:01:05 +0000},
	Date-Modified = {2017-09-07 18:01:05 +0000},
	Local-Url =
	{file://localhost/Users/smiths/LongTermArchives/Work/Research/References/SoftwareEngineering/software-design-automated-testing.pdf},
	Publisher = {International Thomson Computer Press},
	Title = {Software Design, Automated Testing, and Maintenance: A Practical
	Approach},
	Url = {http:// citeseer.ist.psu.edu/428727.html},
	Year = {1995},
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@book{GhezziEtAl2003,
	Address = {Upper Saddle River, NJ, USA},
	Author = {Carlo Ghezzi and Mehdi Jazayeri and Dino Mandrioli},
	Date-Added = {2017-09-05 02:24:05 +0000},
	Date-Modified = {2017-09-05 02:24:05 +0000},
	Edition = {2nd},
	Keywords = {software engineering},
	Publisher = {Prentice Hall},
	Title = {Fundamentals of Software Engineering},
	Year = {2003}}

@book{GriesAndSchneider1993,
	Address = {New York},
	Author = {David Gries and Fred B. Schneider},
	Date-Added = {2017-11-06 15:09:37 +0000},
	Date-Modified = {2017-11-06 15:09:37 +0000},
	Keywords = {discrete mathematics},
	Publisher = {Springer-Verlag Inc.},
	Title = {A logical approach to discrete math},
	Year = {1993}}

@article{MorgPrice,
	Author = {N. R. Morgenstern and V. E. Price},
	Journal = {Géotechnique},
	Number = {15},
	Pages = {79--93},
	Title = {The analysis of the stability of general slip surfaces},
	Year = {January 1965}}

@book{Stress,
	Author = {R. Huston and H. Josephs},
	Title = {Practical stress analysis in engineering design},
	Edition = 3,
	Year = 2008,
	Publisher = {CRC Press}}

repository: smiths/caseStudies

repo_name: smiths/caseStudies

gha_language: C


text:
Automatically generated by Mendeley Desktop 1.19.4
Any changes to this file will be lost if it is regenerated by Mendeley.

BibTeX export options can be customized via Preferences -> BibTeX in Mendeley Desktop

@article{Trente-quatrieme2020,
author = {Trente-quatri{\`{e}}me, Tome and Raymond, A Marcel},
file = {:Users/sepmein/Qsync/Papers/Trente-quatri{\`{e}}me, Raymond{\_}2020{\_}De La Soci{\'{e}}t{\'{e}} J Ea N -J Acques Ro Usseau.pdf:pdf},
pages = {17--18},
title = {{De La Soci{\'{e}}t{\'{e}} J Ea N -J Acques Ro Usseau}},
year = {2020}
}

license: Unlicense

repository: sepmein/references

repo_name: sepmein/references


text:
@article{rose2020reinforcement,
 abstract = {Very often when studying non-equilibrium systems one is interested in analysing dynamical behaviour that occurs with very low probability, so called rare events. In practice, since rare events are by definition atypical, they are often difficult to access in a statistically significant way. What are required are strategies to "make rare events typical" so that they can be generated on demand. Here we present such a general approach to adaptively construct a dynamics that efficiently samples atypical events. We do so by exploiting the methods of reinforcement learning (RL), which refers to the set of machine learning techniques aimed at finding the optimal behaviour to maximise a reward associated with the dynamics. We consider the general perspective of dynamical trajectory ensembles, whereby rare events are described in terms of ensemble reweighting. By minimising the distance between a reweighted ensemble and that of a suitably parametrised controlled dynamics we arrive at a set of methods similar to those of RL to numerically approximate the optimal dynamics that realises the rare behaviour of interest. As simple illustrations we consider in detail the problem of excursions of a random walker, for the case of rare events with a finite time horizon; and the problem of a studying current statistics of a particle hopping in a ring geometry, for the case of an infinite time horizon. We discuss natural extensions of the ideas presented here, including to continuous-time Markov systems, first passage time problems and non-Markovian dynamics.},
 archiveprefix = {arXiv},
 author = {Dominic C. Rose and Jamie F. Mair and Juan P. Garrahan},
 eprint = {2005.12890},
 journal = {ArXiv:2005.12890},
 primaryclass = {cond-mat.stat-mech},
 title = {A reinforcement learning approach to rare trajectory sampling},
 year = {2020}
}

repository: DomCRose/academic-kickstart

repo_name: DomCRose/academic-kickstart


text:
@Misc{Fink-Jensen2015,
author="Fink-Jensen, Jonathan",
title="Book Titles per Capita",
year="2015",
publisher="IISH Dataverse",
url="http://hdl.handle.net/10622/AOQMAZ"
}


text:
@article{kuwahara_production-passage-time_2008,
 abstract = {Given the substantial computational requirements of stochastic simulation, approximation is essential for efficient analysis of any realistic biochemical system. This paper introduces a new approximation method to reduce the computational cost of stochastic simulations of an enzymatic reaction scheme which in biochemical systems often includes rapidly changing fast reactions with enzyme and enzyme-substrate complex molecules present in very small counts. Our new method removes the substrate dissociation reaction by approximating the passage time of the formation of each enzyme-substrate complex molecule which is destined to a production reaction. This approach skips the firings of unimportant yet expensive reaction events, resulting in a substantial acceleration in the stochastic simulations of enzymatic reactions. Additionally, since all the parameters used in our new approach can be derived by the Michaelis-Menten parameters which can actually be measured from experimental data, applications of this approximation can be practical even without having full knowledge of the underlying enzymatic reaction. Here, we apply this new method to various enzymatic reaction systems, resulting in a speedup of orders of magnitude in temporal behavior analysis without any significant loss in accuracy. Furthermore, we show that our new method can perform better than some of the best existing approximation methods for enzymatic reactions in terms of accuracy and efficiency.},
 author = {Kuwahara, Hiroyuki and Myers, Chris J.},
 date = {2008-09},
 doi = {10.1089/cmb.2007.0135},
 issn = {1557-8666},
 journaltitle = {Journal of Computational Biology: A Journal of Computational Molecular Cell Biology},
 keywords = {simulation, stochastic logic, algorithms, enzyme, mathematics, biological models, chemical models, statistical models},
 note = {00000 },
 number = {7},
 pages = {779--792},
 pmid = {18662102},
 shortjournal = {J. Comput. Biol.},
 shorttitle = {Production-passage-time approximation},
 title = {Production-passage-time approximation: a new approximation method to accelerate the simulation process of enzymatic reactions},
 volume = {15}
}


text:
@article{ji2010adaptive,
 author = {Ji, Na and Milkie, Daniel E and Betzig, Eric},
 journal = {Nature methods},
 number = {2},
 pages = {141--147},
 publisher = {Nature Publishing Group},
 title = {Adaptive optics via pupil segmentation for high-resolution imaging in biological tissues},
 volume = {7},
 year = {2010}
}

repository: dmilkie/starter-academic

repo_name: dmilkie/starter-academic