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@inproceedings{duckworth_aamas2016,
	Address = {Singapore},
	Author = {Duckworth, P. and Gatsoulis, Y. and Jovan, F. and Hawes, N. and Hogg, D. C. and Cohn, A. G.},
	Booktitle = {Proc. of the 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2016)},
	Date = {9--13 May},
	Date-Added = {2017-10-15 13:49:11 +0000},
	Date-Modified = {2017-10-15 13:49:11 +0000},
	Title = {Unsupervised Learning of Qualitative Motion Behaviours by a Mobile Robot},
	Year = {2016}}

@inproceedings{dondrup2015tracker,
	Author = {Christian Dondrup and Nicola Bellotto and Ferdian Jovan and Marc Hanheide},
	Booktitle = {International Conference on Robotics and Automation (ICRA) - Workshop on Machine Learning for Social Robotics},
	Date-Added = {2017-10-15 13:47:10 +0000},
	Date-Modified = {2017-10-15 13:47:10 +0000},
	Title = {Real-Time Multisensor People Tracking for Human-Robot Spatial Interaction},
	Year = {2015}}

@article{havoutis13ijrr,
	Author = {Ioannis Havoutis and Subramanian Ramamoorthy},
	Doi = {10.1177/0278364913482016},
	Journal = {The International Journal of Robotics Research},
	Number = {9-10},
	Pages = {1120-1150},
	Title = {Motion planning and reactive control on learnt skill manifolds},
	Volume = {32},
	Year = {2013},
	Bdsk-Url-1 = {http://dx.doi.org/10.1177/0278364913482016}}

@inproceedings{Winkler2015,
	Author = {Winkler, Alexander and Mastalli, Carlos and Havoutis, Ioannis and Focchi, Michele and Caldwell, Darwin G. and Semini, Claudio},
	Booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
	Title = {{Planning and Execution of Dynamic Whole-Body Locomotion for a Hydraulic Quadruped on Challenging Terrain}},
	Year = {2015}}

@inproceedings{havoutis15clawar,
	Author = {I. Havoutis and D. G. Caldwell and C. Semini},
	Booktitle = {Internatinal Conference on Climbing and Walking Machines (CLAWAR)},
	Title = {Lidar-based navigation-level path planning for field-capable legged robots},
	Year = {2015}}

@inproceedings{Mastalli2015,
	Author = {Mastalli, Carlos and Winkler, Alexander and Havoutis, Ioannis and Caldwell, Darwin G. and Semini, Claudio},
	Booktitle = {{IEEE International Conference on Technologies for Practical Robot Applications (TEPRA)}},
	Title = {{On-line and On-board Planning and Perception for Quadrupedal Locomotion}},
	Year = {2015}}

@inproceedings{Havoutis16SSRR,
	Address = {Lausanne, Switzerland},
	Author = {Havoutis, I. and Calinon, S.},
	Booktitle = {IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR)},
	Month = {October},
	Title = {Learning assistive teleoperation behaviors from demonstration},
	Year = {2016}}

@article{Zeestraten2017-RAL,
	Author = {Zeestraten, M. J. A. and Havoutis, I. and Silv\'erio, J. and Calinon, S. and Caldwell, D. G.},
	Doi = {10.1109/LRA.2017.2657001},
	Journal = {{IEEE} Robotics and Automation Letters ({RA-L})},
	Month = {June},
	Number = {3},
	Pages = {1240--1247},
	Title = {An Approach for Imitation Learning on {R}iemannian Manifolds},
	Volume = {2},
	Year = {2017},
	Bdsk-Url-1 = {http://dx.doi.org/10.1109/LRA.2017.2657001}}

@inproceedings{Zeestraten17IROS,
	Address = {Vancouver, Canada},
	Author = {Zeestraten, M. J. A. and Havoutis, I. and Calinon, S. and Caldwell, D. G.},
	Booktitle = {Proc. {IEEE/RSJ} Intl Conf. on Intelligent Robots and Systems ({IROS})},
	Month = {September},
	Pages = {73--78},
	Title = {Learning Task-Space Synergies using Riemannian Geometry},
	Year = {2017}}

@inproceedings{Havoutis17ICRA,
	Address = {Singapore},
	Author = {Havoutis, I. and Calinon, S.},
	Booktitle = {Proc. {IEEE} Intl Conf. on Robotics and Automation ({ICRA})},
	Month = {May-June},
	Pages = {1534--1540},
	Title = {Supervisory teleoperation with online learning and optimal control},
	Year = {2017}}

@inproceedings{Mastalli17ICRA,
	Address = {Singapore},
	Author = {Mastalli, C. and Focchi, M. and Havoutis, I. and Radulescu, A. and Calinon, S. and Buchli, J. and Caldwell, D. G. and Semini, C.},
	Booktitle = {Proc. {IEEE} Intl Conf. on Robotics and Automation ({ICRA})},
	Month = {May-June},
	Pages = {1096--1103},
	Title = {Trajectory and Foothold Optimization using Low-Dimensional Models for Rough Terrain Locomotion},
	Year = {2017}}

@inproceedings{kunze17ecmr,
	Address = {Paris, France},
	Author = {Lars Kunze and Mohan Sridharan and Christos Dimitrakakis and Jeremy Wyatt},
	Booktitle = {European Conference on Mobile Robots (ECMR) 2017},
	Month = {September},
	Note = {Accepted for publication},
	Options = {asterisk},
	Title = {Adaptive Sampling-based View Planning under Time Constraints},
	Year = 2017}

@inproceedings{tufts17reg,
	Address = {Arlington, Virgina, US},
	Author = {Lars Kunze and Tom Williams and Nick Hawes and Matthias Scheutz},
	Booktitle = {AAAI Fall Symposium 2017 on Artificial Intelligence for Human-Robot Interaction},
	Keywords = {workshop},
	Month = {November, 9--11},
	Note = {Submitted},
	Options = {asterisk},
	Title = {Spatial Referring Expression Generation for {HRI}: Algorithms and Evaluation Framework},
	Year = {2017}}

@article{kunze15aij,
	Annotation = {{\textbf{Impact factor: 2.709 (Accepted in the Journal Presentation Track of ICAPS 2015)}}},
	Author = {Lars Kunze and Michael Beetz},
	Doi = {http://dx.doi.org/10.1016/j.artint.2014.12.004},
	Journal = {Artificial Intelligence, Special Issue on AI and Robotic},
	Options = {asterisk, diamond},
	Title = {Envisioning the Qualitative Effects of Robot Manipulation Actions using Simulation-based Projections},
	Year = 2015,
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.artint.2014.12.004}}



@inproceedings{kunze14topdown,
	Address = {Chicago, Illinois, US},
	Annotation = {Cognitive Robotics Best Paper Award Finalist (3/755)},
	Author = {Lars Kunze and Chris Burbridge and Marina Alberti and Akshaya Tippur and John Folkesson and Patric Jensfelt and Nick Hawes},
	Booktitle = {2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
	Month = {September, 14--18},
	Options = {asterisk, diamond},
	Title = {Combining Top-down Spatial Reasoning and Bottom-up Object Class Recognition for Scene Understanding},
	Year = {2014}}

@inproceedings{kunze14indirect,
	Address = {Hong Kong, China},
	Author = {Lars Kunze and Keerthi Kumar Doreswamy and Nick Hawes},
	Booktitle = {IEEE International Conference on Robotics and Automation (ICRA 2014)},
	Month = {May 31 - June 7},
	Options = {asterisk},
	Title = {Using Qualitative Spatial Relations for Indirect Object Search},
	Year = {2014}}

@inproceedings{kunze14bootstrapping,
	Address = {Stanford University in Palo Alto, California, US},
	Author = {Lars Kunze and Chris Burbridge and Nick Hawes},
	Booktitle = {AAAI Spring Symposium 2014 on Qualitative Representations for Robots},
	Keywords = {workshop},
	Month = {March, 24--26},
	Options = {asterisk},
	Title = {Bootstrapping Probabilistic Models of Qualitative Spatial Relations for Active Visual Object Search},
	Year = {2014}}

@inproceedings{kunze12objsearch,
	Address = {St. Paul, MN, USA},
	Author = {Lars Kunze and Michael Beetz and Manabu Saito and Haseru Azuma and Kei Okada and Masayuki Inaba},
	Booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
	Month = {May 14--18},
	Options = {asterisk},
	Title = {Searching Objects in Large-scale Indoor Environments: A Decision-thereotic Approach},
	Year = {2012}}



@inproceedings{aloof@icra17,
  author = {Jay Young and Lars Kunze and Valerio Basile and Elena Cabrio and Nick Hawes},
  title = {Semantic Web-Mining and Deep Vision for Lifelong Object Discovery},
  booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
  month = {May 29--June 3},
  year = 2017,
  pages = {},
  address = {Singapore},
}



@inproceedings {aloof@ecai16,
  title = {Towards Lifelong Object Learning by Integrating Situated Robot
                  Perception and Semantic Web Mining},
  booktitle = {23rd European Conf. on Artificial Intelligence  (ECAI{\textquoteright}16)},
  year = {2016},
  address = {The Hague, Netherlands},
  author = {Jay Young and Valerio Basile and Lars Kunze and Elena Cabrio and Nick Hawes},
  annotation = {{\textbf{Acceptance rate: 27\%}}}
}



@article{strands@ram,
	Author = {Nick Hawes and Chris Burbridge and Ferdian Jovan and Lars Kunze. et.al.},
	Date-Added = {2016-09-20 10:07:47 +0000},
	Date-Modified = {2017-10-15 14:07:39 +0000},
	Journal = {{IEEE} Robotics \& Automation Magazine (RAM)},
	Month = {September},
	Number = {3},
	Title = {The {STRANDS} Project: Long-Term Autonomy in Everyday Environments},
	Url = {http://arxiv.org/abs/1604.04384},
	Volume = {24},
	Year = {2017},
	Bdsk-Url-1 = {http://arxiv.org/abs/1604.04384}}


@inproceedings{AmayoICRA2016,
	Address = {Stockholm, Sweden},
	Author = {Amayo, Paul and Pini{\'e}s, Pedro and Paz, Lina Maria and Newman, Paul},
	Booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
	Month = {May},
	Pdf = {http://www.robots.ox.ac.uk/~mobile/Papers/ICRA2016_amayo.pdf},
	Title = {A Unified Representation for Application of Architectural Constraints in Large-Scale Mapping},
	Year = {2016}}



@inproceedings{jpulido2015NowOrLater,
	Abstract = {
        In planning for deliberation or navigation in real-world robotic systems, 
        one of the big challenges is to cope with change. 
        It lies in the nature of planning that it has to make assumptions about the future state of the world, 
        and the robot's chances of successively accomplishing actions in this future. Hence, a robot's plan can only be as good as its predictions about the world.
        In this paper, we present a novel approach to specifically represent changes that stem from \emph{periodic} events in the environment (e.g. a door being opened or closed), 
        which impact on the success probability of planned actions. 
        We show that our approach to model the probability of action success as a set of superimposed periodic processes allows the robot to predict action outcomes 
        in a long-term data obtained in two real-life offices better than a static model. 
        We furthermore discuss and showcase how this knowledge gathered can be successfully employed in a probabilistic planning framework to devise better navigation plans.
        The key contributions of this paper are (i) the formation of the spectral model of action outcomes from non-uniform sampling, 
        the (ii) analysis of its predictive power using two long-term datasets, and (iii) the application of the predicted outcomes in an MDP-based planning framework.},
	Author = {Pulido Fentanes, Jaime and Lacerda, Bruno and Krajn{\'\i}k, Tom{\'a}{\v s} and Hawes, Nick and Hanheide, Marc},
	Booktitle = {International Conference on Robotics and Automation (ICRA)},
	Date-Added = {2015-06-26 17:46:35 +0000},
	Date-Modified = {2015-06-26 17:46:35 +0000},
	Title = {Now or later? Predicting and Maximising Success of Navigation Actions from Long-Term Experience},
	Year = {2015}}

@article{lacerda_ijrr19, 
author = {Bruno Lacerda and Fatma Faruq and David Parker and Nick Hawes}, 
Title = {Probabilistic Planning with Formal Performance Guarantees for Mobile Service Robots}, 
journal={International Journal of Robotics Research},
Year = {2019},
volume = {38},
number = {9}
}

@article{Faeulhammer:2016,
	Addendum = {Cites: 3.},
	Author = {Faeulhammer, T. and Ambrus, R. and Burbridge, C. and Zillich, M. and Folkesson, J. and Hawes, N. and Jensfelt, P. and Vincze, M.},
	Date-Added = {2016-02-04 09:57:30 +0000},
	Date-Modified = {2017-02-07 22:27:41 +0000},
	Doi = {10.1109/LRA.2016.2522086},
	Journal = {IEEE Robotics and Automation Letters (RA-L)},
	Keywords = {Cameras;Heuristic algorithms;Mobile robots;Robot vision systems;Solid modeling;Three-dimensional displays;Autonomous Agents;Motion and Path Planning;Object detection;RGB-D Perception;Visual Learning;categorization},
	Number = {1},
	Pages = {26 - 33},
	Title = {Autonomous Learning of Object Models on a Mobile Robot},
	Volume = {2},
	Year = {2016},
	Bdsk-Url-1 = {http://dx.doi.org/10.1109/LRA.2016.2522086}}
	
@article{GPD1,
abstract = {Recently, a number of grasp detection methods have been proposed that can be used to localize robotic grasp configurations directly from sensor data without estimating object pose. The underlying idea is to treat grasp perception analogously to object detection in computer vision. These methods take as input a noisy and partially occluded RGBD image or point cloud and produce as output pose estimates of viable grasps, without assuming a known CAD model of the object. Although these methods generalize grasp knowledge to new objects well, they have not yet been demonstrated to be reliable enough for wide use. Many grasp detection methods achieve grasp success rates (grasp successes as a fraction of the total number of grasp attempts) between 75{\%} and 95{\%} for novel objects presented in isolation or in light clutter. Not only are these success rates too low for practical grasping applications, but the light clutter scenarios that are evaluated often do not reflect the realities of real world grasping. This paper proposes a number of innovations that together result in a significant improvement in grasp detection performance. The specific improvement in performance due to each of our contributions is quantitatively measured either in simulation or on robotic hardware. Ultimately, we report a series of robotic experiments that average a 93{\%} end-to-end grasp success rate for novel objects presented in dense clutter.},
archivePrefix = {arXiv},
arxivId = {1706.09911v1},
author = {ten Pas, Andreas and Gualtieri, Marcus and Saenko, Kate and Platt, Robert},
doi = {10.1177/0278364917735594},
eprint = {1706.09911v1},
file = {:Users/mfinean/Library/Application Support/Mendeley Desktop/Downloaded/Pas - Unknown - Grasp Pose Detection in Point Clouds Journal Title XX(X)1-17 c.pdf:pdf},
issn = {0278-3649},
journal = {The International Journal of Robotics Research},
month = {dec},
number = {13-14},
pages = {1455--1473},
title = {{Grasp Pose Detection in Point Clouds}},
url = {www.sagepub.com/ http://journals.sagepub.com/doi/10.1177/0278364917735594},
volume = {36},
year = {2017}
}

@inproceedings{GPD2,
abstract = {This paper considers the problem of grasp pose detection in point clouds. We follow a general algorithmic structure that first generates a large set of 6-DOF grasp candidates and then classifies each of them as a good or a bad grasp. Our focus in this paper is on improving the second step by using depth sensor scans from large online datasets to train a convolutional neural network. We propose two new representations of grasp candidates, and we quantify the effect of using prior knowledge of two forms: instance or category knowledge of the object to be grasped, and pretraining the network on simulated depth data obtained from idealized CAD models. Our analysis shows that a more informative grasp candidate representation as well as pretraining and prior knowledge significantly improve grasp detection. We evaluate our approach on a Baxter Research Robot and demonstrate an average grasp success rate of 93{\%} in dense clutter. This is a 20{\%} improvement compared to our prior work.},
archivePrefix = {arXiv},
arxivId = {1603.01564v2},
author = {Gualtieri, Marcus and ten Pas, Andreas and Saenko, Kate and Platt, Robert},
booktitle = {2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
doi = {10.1109/IROS.2016.7759114},
eprint = {1603.01564v2},
file = {:Users/mfinean/Library/Application Support/Mendeley Desktop/Downloaded/Gualtieri et al. - Unknown - High precision grasp pose detection in dense clutter.pdf:pdf},
isbn = {978-1-5090-3762-9},
month = {oct},
pages = {598--605},
publisher = {IEEE},
title = {{High precision grasp pose detection in dense clutter}},
url = {https://github.com/atenpas/gpd http://ieeexplore.ieee.org/document/7759114/},
year = {2016}
}

@article{PrinceWilliamArticle,
   author = {David Lynch},
   journal = {Oxford Mail},
   month = {10},
   title = {VIDEO: Prince William thanks robot for giving him plant in Oxford},
   url = {https://www.oxfordmail.co.uk/news/17944896.video-prince-william-thanks-robot-giving-plant-oxford/},
   year = {2019}
}

@misc{OrionWebpageMeetTheTeam,
   title = {ORIon - Meet the Team},
   url = {https://ori.ox.ac.uk/student-teams/team-orion/meet-the-team/},
   year = {2019}
}

@article{oord2016wavenet,
  title={Wavenet: A generative model for raw audio},
  author={Oord, Aaron van den and Dieleman, Sander and Zen, Heiga and Simonyan, Karen and Vinyals, Oriol and Graves, Alex and Kalchbrenner, Nal and Senior, Andrew and Kavukcuoglu, Koray},
  journal={arXiv preprint arXiv:1609.03499},
  year={2016}
}

@article{miller1995wordnet,
  title={WordNet: a lexical database for English},
  author={Miller, George A},
  journal={Communications of the ACM},
  volume={38},
  number={11},
  pages={39--41},
  year={1995},
  publisher={ACM New York, NY, USA}
}

@article{ren2015faster,
  title={Faster r-cnn: Towards real-time object detection with region proposal networks},
  author={Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian},
  journal={Advances in neural information processing systems},
  volume={28},
  pages={91--99},
  year={2015}
}



@inproceedings{winfield2017,
author="Winfield, Alan F. T.
and Jirotka, Marina",
editor="Gao, Yang
and Fallah, Saber
and Jin, Yaochu
and Lekakou, Constantina",
title="The Case for an Ethical Black Box",
booktitle="Towards Autonomous Robotic Systems",
year="2017",
publisher="Springer International Publishing",
address="Cham",
pages="262--273",
abstract="This paper proposes that robots and autonomous systems should be equipped with the equivalent of a Flight Data Recorder to continuously record sensor and relevant internal status data. We call this an ethical black box. We argue that an ethical black box will be critical to the process of discovering why and how a robot caused an accident, and thus an essential part of establishing accountability and responsibility. We also argue that without the transparency afforded by an ethical black box, robots and autonomous systems are unlikely to win public trust.",
isbn="978-3-319-64107-2"
}

@inproceedings{finean2021simultaneous,
      title={Simultaneous Scene Reconstruction and Whole-Body Motion Planning for Safe Operation in Dynamic Environments}, 
      author={Mark Nicholas Finean and Wolfgang Merkt and Ioannis Havoutis},
      booktitle = {IEEE International Conference on Intelligent Robots and Systems (IROS)},
		  address = {Czech Republic},
      year={2021}
}

@article{finean2021i,
  title={Where Should I Look? Optimised Gaze Control for Whole-Body Collision Avoidance in Dynamic Environments},
  author={Finean, Mark Nicholas and Merkt, Wolfgang and Havoutis, Ioannis},
  journal={IEEE Robotics and Automation Letters},
  year={2021},
  note={Under review (arXiv preprint arXiv:2109.04721)}
}


@misc{finean2021predicted,
      title={Predicted Composite Signed-Distance Fields for Real-Time Motion Planning in Dynamic Environments}, 
      author={Mark Nicholas Finean and Wolfgang Merkt and Ioannis Havoutis},
      year={2021},
      eprint={2008.00969},
      archivePrefix={arXiv},
      primaryClass={cs.RO}
}

@misc{yolov7,
	doi = {10.48550/ARXIV.2207.02696},
	
	url = {https://arxiv.org/abs/2207.02696},
	
	author = {Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
	
	keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
	
	title = {YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors},
	
	publisher = {arXiv},
	
	year = {2022},
	
	copyright = {arXiv.org perpetual, non-exclusive license}
}

@misc{strongsort,
	doi = {10.48550/ARXIV.2202.13514},
	
	url = {https://arxiv.org/abs/2202.13514},
	
	author = {Du, Yunhao and Song, Yang and Yang, Bo and Zhao, Yanyun},
	
	keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
	
	title = {StrongSORT: Make DeepSORT Great Again},
	
	publisher = {arXiv},
	
	year = {2022},
	
	copyright = {Creative Commons Attribution 4.0 International}
}