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Publications @ A2I

2020

  • [PDF] D. Barnes, M. Gadd, P. Murcutt, P. Newman, and I. Posner, “The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Paris, 2020.
    [Bibtex]
  • [PDF] M. Engelcke, A. R. Kosiorek, O. Parker Jones, and I. Posner, “GENESIS: Generative Scene Inference and Sampling of Object-Centric Latent Representations,” International Conference on Learning Representations (ICLR), 2020.
    [Bibtex]
  • [PDF] D. Barnes and I. Posner, “Under the Radar: Learning to Predict Robust Keypoints for Odometry Estimation and Metric Localisation in Radar,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Paris, 2020.
    [Bibtex]

2019

  • [PDF] E. Wagstaff, F. Fuchs, M. Engelcke, I. Posner, and M. A. Osborne, “On the Limitations of Representing Functions on Sets,” in Proceedings of the 36th International Conference on Machine Learning, Long Beach, California, USA, 2019, p. 6487–6494.
    [Bibtex]
  • [PDF] R. Weston, S. Cen, P. Newman, and I. Posner, “Probably Unknown: Deep Inverse Sensor Modelling Radar,” in IEEE International Conference on Robotics and Automation (ICRA), Montreal, Canada, 2019.
    [Bibtex]
  • [PDF] D. Barnes, R. Weston, and I. Posner, “Masking by Moving: Learning Distraction-Free Radar Odometry from Pose Information,” in Conference on Robot Learning (CoRL), 2019.
    [Bibtex]
  • [PDF] Y. Wu, S. Kasewa, O. Groth, S. Salter, L. Sun, O. P. Jones, and I. Posner, “Imagine That! Leveraging Emergent Affordances for Tool Synthesis in Reaching Tasks,” arXiv preprint arXiv:1909.13561, 2019.
    [Bibtex]
  • [PDF] F. Fuchs, A. Kosiorek, L. Sun, O. Parker Jones, and I. Posner, “End-to-end Recurrent Multi-Object Tracking and Trajectory Prediction with Relational Reasoning,” in Sets and Parts Workshop at the Conference on Neural Information Processing Systems (NeurIPS), 2019.
    [Bibtex]
  • [PDF] F. Fuchs, O. Groth, A. Kosiorek, A. Bewley, M. Wulfmeier, A. Vedaldi, and I. Posner, “Scrutinizing and De-Biasing Intuitive Physics with Neural Stethoscopes,” in Proceedings of the British Machine Vision Conference (BMVC), 2019.
    [Bibtex]

2018

  • [PDF] C. Gurau, A. Bewley, and I. Posner, “Dropout Distillation for Efficiently Estimating Model Confidence,” arXiv preprint arXiv:1809.10562, 2018.
    [Bibtex]
  • [PDF] L. Marchegiani and I. Posner, “Long-Term Driving Behaviour Modelling for Driver Identification,” in IEEE International Conference on Intelligent Transportation Systems (ITSC), Maui, Hawaii, USA, 2018.
    [Bibtex]
  • [PDF] O. Groth, F. B. Fuchs, I. Posner, and A. Vedaldi, “ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking,” in The European Conference on Computer Vision (ECCV), 2018.
    [Bibtex]
  • [PDF] A. R. Kosiorek, H. Kim, I. Posner, and Y. W. Teh, “Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects,” arXiv preprint arXiv:1806.01794, 2018.
    [Bibtex]
  • [PDF] D. Barnes, W. Maddern, G. Pascoe, and I. Posner, “Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Brisbane, 2018.
    [Bibtex]
  • [PDF] K. Shiarlis, M. Wulfmeier, S. Salter, S. Whiteson, and I. Posner, “TACO: Learning Task Decomposition via Temporal Alignment for Control,” Proceedings of the 35th International Conference on Machine Learning, 2018.
    [Bibtex]
  • [PDF] M. Wulfmeier, A. Bewley, and I. Posner, “Incremental Adversarial Domain Adaptation for Continually Changing Environments,” in IEEE International Conference on Robotics and Automation (ICRA), 2018.
    [Bibtex]

2017

  • [PDF] N. Dhir, A. R. Kosiorek, and I. Posner, “Bayesian Delay Embeddings for Dynamical Systems,” in NIPS Timeseries Workshop, 2017.
    [Bibtex]
  • [PDF] M. Wulfmeier, I. Posner, and P. Abbeel, “Mutual Alignment Transfer Learning,” in Conference on Robot Learning, 2017.
    [Bibtex]
  • [PDF] A. R. Kosiorek, A. Bewley, and I. Posner, “Hierarchical Attentive Recurrent Tracking,” in Neural Information Processing Systems, 2017.
    [Bibtex]
  • [PDF] [DOI] M. Wulfmeier, D. Rao, D. Z. Wang, P. Ondruska, and I. Posner, “Large-scale cost function learning for path planning using deep inverse reinforcement learning,” The International Journal of Robotics Research, p. 278364917722396, 2017.
    [Bibtex]
  • [PDF] C. Gurau, D. Rao, C. H. Tong, and I. Posner, “Learn from Experience: Probabilistic Prediction of Perception Performance to Avoid Failure,” The International Journal of Robotics Research, 2017.
    [Bibtex]
  • [PDF] J. Hawke, A. Bewley, and I. Posner, “What Makes a Place? Building Bespoke Place Dependent Object Detectors for Robotics,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2017.
    [Bibtex]
  • [PDF] M. Wulfmeier, A. Bewley, and I. Posner, “Addressing Appearance Change in Outdoor Robotics with Adversarial Domain Adaptation,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2017.
    [Bibtex]
  • [PDF] J. Dequaire, P. Ondrúška, D. Rao, D. Wang, and I. Posner, “Deep tracking in the wild: End-to-end tracking using recurrent neural networks,” The International Journal of Robotics Research, 2017.
    [Bibtex]
  • [PDF] M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong, and I. Posner, “Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2017.
    [Bibtex]
  • [PDF] D. Barnes, W. Maddern, and I. Posner, “Find Your Own Way: Weakly-Supervised Segmentation of Path Proposals for Urban Autonomy,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Singapore, 2017.
    [Bibtex]
  • [PDF] L. Marchegiani and I. Posner, “Leveraging the Urban Soundscape: Auditory Perception for Smart Vehicles,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Singapore, 2017.
    [Bibtex]

2016

  • [PDF] M. Wulfmeier, D. Rao, and I. Posner, “Incorporating Human Domain Knowledge into Large Scale Cost Function Learning,” in Neural Information Processing Systems Conference, Deep Reinforcement Learning Workshop, 2016.
    [Bibtex]
  • [PDF] J. Dequaire, D. Rao, P. Ondruska, D. Zeng Wang, and I. Posner, “Deep Tracking on the Move: Learning to Track the World from a Moving Vehicle using Recurrent Neural Networks,” ArXiv e-prints, 2016.
    [Bibtex]
  • [PDF] O. Bartlett, C. Gurau, L. Marchegiani, and I. Posner, “Enabling Intelligent Energy Management for Robots using Publicly Available Maps,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, South Korea, 2016.
    [Bibtex]
  • [PDF] C. Gurau, C. H. Tong, and I. Posner, “Fit for purpose? Predicting Perception Performance based on Past Experience,” in International Symposium on Experimental Robotics (ISER), Tokyo, Japan, 2016.
    [Bibtex]
  • [PDF] M. Wulfmeier, D. Z. Wang, and I. Posner, ” Watch This: Scalable Cost-Function Learning for Path Planning in Urban Environments ,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016.
    [Bibtex]
  • [PDF] P. Ondruska, J. Dequaire, D. Zeng Wang, and I. Posner, “End-to-End Tracking and Semantic Segmentation Using Recurrent Neural Networks,” in Robotics: Science and Systems, Workshop on Limits and Potentials of Deep Learning in Robotics, 2016.
    [Bibtex]
  • [PDF] U. Schwesinger, M. Bürki, J. Timpner, S. Rottmann, L. Wolf, L. M. Paz, H. Grimmett, I. Posner, P. Newman, C. Häne, L. Heng, G. H. Lee, T. Sattler, M. Pollefeys, M. Allodi, F. Valenti, K. Mimura, B. Goebelsmann, and R. Siegwart, “Automated Valet Parking and Charging for e-Mobility–-Results of the V-Charge Project,” in Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Gothenburg, Sweden, 2016.
    [Bibtex]
  • [PDF] P. Ondruska, J. Dequaire, Zeng Wang Dominic, and I. Posner, “End-to-End Tracking and Semantic Segmentation Using Recurrent Neural Networks,” ArXiv e-prints, 2016.
    [Bibtex]
  • [PDF] J. Dequaire, C. H. Tong, W. Churchill, and I. Posner, “Off the Beaten Track: Predicting Localisation Performance in Visual Teach and Repeat,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, 2016.
    [Bibtex]
  • [PDF] T. Scott, A. A. Morye, P. Piniés, L. M. Paz, I. Posner, and P. Newman, “Choosing a Time and Place for Calibration of Lidar-Camera Systems,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, 2016.
    [Bibtex]
  • [PDF] P. Ondruska and I. Posner, “Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks,” in The Thirtieth AAAI Conference on Artificial Intelligence (AAAI), Phoenix, Arizona USA, 2016.
    [Bibtex]

2015

  • [PDF] [DOI] B. Mathibela, P. Newman, and I. Posner, “Reading the Road: Road Marking Classification and Interpretation,” IEEE Trans. Intelligent Transportation Systems, vol. 16, iss. 4, p. 2072–2081, 2015.
    [Bibtex]
  • [PDF] M. Wulfmeier, P. Ondruska, and I. Posner, “Maximum Entropy Deep Inverse Reinforcement Learning,” in Neural Information Processing Systems Conference, Deep Reinforcement Learning Workshop, Montreal, Canada, 2015.
    [Bibtex]
  • [PDF] T. Scott, A. A. Morye, P. Piniés, L. M. Paz, I. Posner, and P. Newman, “Exploiting Known Unknowns: Scene Induced Cross-Calibration of Lidar-Stereo Systems,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany, 2015.
    [Bibtex]
  • [PDF] H. Grimmett, R. Triebel, R. Paul, and I. Posner, “Introspective Classification for Robot Perception,” International Journal of Robotics Research (IJRR), 2015.
    [Bibtex]
  • [PDF] D. Barnes, W. Maddern, and I. Posner, “Exploiting 3D Semantic Scene Priors for Online Traffic Light Interpretation,” in Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Seoul, South Korea, 2015.
    [Bibtex]
  • [PDF] J. Hawke, C. Gurau, C. H. Tong, and I. Posner, “Wrong Today, Right Tomorrow: Experience-Based Classification for Robot Perception,” in Field and Service Robotics (FSR), Toronto, ON, Canada, 2015.
    [Bibtex]
  • [PDF] P. Nelson, W. Churchill, I. Posner, and P. Newman, “From Dusk till Dawn: Localisation at Night using Artificial Light Sources,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA, 2015.
    [Bibtex]
  • [PDF] W. Churchill, C. H. Tong, C. Gurau, I. Posner, and P. Newman, “Know Your Limits: Embedding Localiser Performance Models in Teach and Repeat Maps,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA, 2015.
    [Bibtex]
  • [PDF] H. Grimmett, M. Buerki, L. Paz, P. Piniés, P. Furgale, I. Posner, and P. Newman, “Integrating Metric and Semantic Maps for Vision-Only Automated Parking,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA, 2015.
    [Bibtex]
  • [PDF] L. Berczi, I. Posner, and T. D. Barfoot, “Learning to Assess Terrain from Human Demonstration Using an Introspective Gaussian-Process Classifier,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA, 2015.
    [Bibtex]
  • [PDF] P. Ondruska, C. Gurau, L. Marchegiani, C. H. Tong, and I. Posner, “Scheduled Perception for Energy-Efficient Path Following,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA, 2015.
    [Bibtex]
  • [DOI] D. Z. Wang, I. Posner, and P. Newman, “Model-Free Detection and Tracking of Dynamic Objects with 2D Lidar,” The International Journal of Robotics Research (IJRR), vol. 34, iss. 7, pp. 1039-1063, 2015.
    [Bibtex]
  • [PDF] D. Z. Wang and I. Posner, “Voting for Voting in Online Point Cloud Object Detection,” in Proceedings of Robotics: Science and Systems, Rome, Italy, 2015.
    [Bibtex]

2014

  • [PDF] C. Gurau, J. Hawke, C. H. Tong, and I. Posner, “Learning on the Job: Improving Robot Perception Through Experience,” in Neural Information Processing Systems (NIPS) Workshop on “Autonomously Learning Robots”, Montreal, Quebec, Canada, 2014.
    [Bibtex]
  • [PDF] P. Ondruska and I. Posner, “The Route Not Taken: Driver-Centric Estimation of Electric Vehicle Range,” in Proceedings of the 24th International Conference on Automated Planning and Scheduling (ICAPS), Portsmouth, NH, USA, 2014.
    [Bibtex]
  • [PDF] P. Ondruska and I. Posner, “Probabilistic Attainability Maps: Efficiently Predicting Driver-Specific Electric Vehicle Range,” in IEEE Intelligent Vehicles Symposium (IV), Dearborn, MI, USA, 2014.
    [Bibtex]

2013

  • [PDF] P. Furgale, U. Schwesinger, M. Rufli, W. Derendarz, H. Grimmett, P. Mühlfellner, S. Wonneberger, J. T. S. Rottmann, B. Li, B. Schmidt, T. N. Nguyen, E. Cardarelli, S. Cattani, S. Brüning, S. Horstmann, M. Stellmacher, H. Mielenz, K. Köser, M. Beermann, C. Häne, L. Heng, G. H. Lee, F. Fraundorfer, R. Iser, R. Triebel, I. Posner, P. Newman, L. Wolf, M. Pollefeys, S. Brosig, J. Effertz, C. Pradalier, and R. Siegwart, “Toward Automated Driving in Cities using Close-to-Market Sensors, an Overview of the V-Charge Project,” in IEEE Intelligent Vehicles Symposium (IV), Gold Coast, Australia, 2013, p. 809–816.
    [Bibtex]
  • [PDF] R. Triebel, H. Grimmett, R. Paul, and I. Posner, “Driven Learning for Driving: How Introspection Improves Semantic Mapping,” in International Symposium on Robotics Research (ISRR), Singapore, 2013.
    [Bibtex]
  • [PDF] D. Z. Wang, I. Posner, and P. Newman, “A New Approach to Model-Free Tracking with 2D Lidar,” in Proceedings of the International Symposium on Robotics Research (ISRR), Singapore, 2013.
    [Bibtex]
  • [PDF] R. Triebel, H. Grimmett, R. Paul, and I. Posner, “Introspective Active Learning for Scalable Semantic Mapping,” in Workshop on Active Learning in Robotics: Exploration, Curiosity and Interaction. Robotics Science and Systems (RSS), 2013.
    [Bibtex]
  • R. Triebel, H. Grimmett, and I. Posner, “Confidence Boosting: Improving the Introspectiveness of a Boosted Classifier for Efficient Learning,” in Workshop on Autonomous Learing. IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany, 2013.
    [Bibtex]
  • [PDF] H. Grimmett, R. Paul, R. Triebel, and I. Posner, “Knowing When We Don’t Know: Introspective Classification for Mission-Critical Decision Making,” in Proc. IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany, 2013.
    [Bibtex]

2012

  • J. Velez, G. Hemann, A. S. Huang, I. Posner, and N. Roy, “Modelling Observation Correlations for Active Exploration and Robust Object Detection,” JAIR Journal of Artificial Intelligence Research, vol. 44, pp. 423-453, 2012.
    [Bibtex]
  • [PDF] B. Mathibela, M. A. Osborne, I. Posner, and P. Newman, “Can Priors Be Trusted? Learning to Anticipate Roadworks,” in Proc. IEEE Conference on Intelligent Transportation Systems (ITSC), Anchorage, AK, USA, 2012.
    [Bibtex]
  • [PDF] D. Z. Wang, I. Posner, and P. Newman, “What Could Move? Finding Cars, Pedestrians and Bicyclists in 3D Laser Data,” in Proc. IEEE International Conference on Robotics and Automation (ICRA), Minnesota, USA, 2012.
    [Bibtex]

2011

  • [PDF] J. Velez, G. Hemann, A. S. Huang, I. Posner, and N. Roy, “Active Exploration for Robust Object Detection,” in International Joint Conference on Artificial Intelligence (IJCAI), Barcelona, Spain, 2011.
    [Bibtex]
  • [PDF] J. Velez, G. Hemann, A. S. Huang, I. Posner, and N. Roy, “Planning to Perceive: Exploiting Mobility For Robust Object Detection,” in International Conference on Automated Planning and Scheduling, Freiburg, Germany, 2011.
    [Bibtex]
  • [PDF] M. Smith, I. Posner, and P. Newman, “Adaptive compression for 3D laser data,” The International Journal of Robotics Research, vol. 30, iss. 7, pp. 914-935, 2011.
    [Bibtex]

2010

  • [PDF] I. Posner, P. Corke, and P. Newman, “Using Text-Spotting to Query the World,” in Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2010.
    [Bibtex]
  • [PDF] M. Smith, I. Posner, and P. Newman, “Generating Implicit Surfaces from Lidar Data,” in Towards Autonomous Robotic Systems, Plymouth, UK, 2010.
    [Bibtex]
  • [PDF] M. Smith, I. Posner, and P. Newman, “Efficient Non-Parametric Surface Representations Using Active Sampling for Push Broom Laser Data,” in Proceedings of Robotics: Science and Systems VI, Zaragoza, Spain, 2010.
    [Bibtex]

2009

  • [PDF] [DOI] P. Newman, G. Sibley, M. Smith, M. Cummins, A. Harrison, C. Mei, I. Posner, R. Shade, D. Schroeter, L. Murphy, W. Churchill, D. Cole, and I. Reid, “Navigating, Recognising and Describing Urban Spaces With Vision and Laser,” The International Journal of Robotics Research, vol. 28, 2009.
    [Bibtex]
  • [PDF] [DOI] I. Posner, M. Cummins, and P. Newman, “A generative framework for fast urban labeling using spatial and temporal context,” Autonomous Robots, vol. 26, iss. 2-3, pp. 153-170, 2009.
    [Bibtex]

2008

  • [PDF] I. Posner, M. Cummins, and P. Newman, “Fast Probabilistic Labeling of City Maps,” in Proceedings of Robotics: Science and Systems IV, Zurich, Switzerland, 2008.
    [Bibtex]
  • [PDF] [DOI] I. Posner, D. Schroeter, and P. Newman, “Online generation of scene descriptions in urban environments,” Robotics and Autonomous Systems, vol. 56, iss. 11, pp. 901-914, 2008.
    [Bibtex]

2007

  • [PDF] P. Newman, M. Chandran-Ramesh, D. Cole, M. Cummins, A. Harrison, I. Posner, and D. Schroeter, “Describing, Navigating and Recognising Urban Spaces – Building An End-to-End SLAM System,” in Proc. of the Int. Symposium of Robotics Research (ISRR), Hiroshima,Japan, 2007.
    [Bibtex]
  • [PDF] I. Posner, D. Schroeter, and P. Newman, “Describing Composite Urban Workspaces,” in Proc. of the Int. Conference on Robotics and Automation, Rome, 2007.
    [Bibtex]

2006

  • [PDF] I. Posner, D. Schroeter, and P. Newman, “Using Scene Similarity for Place Labelling,” in Proc. of the Int. Symposium on Experimental Robotics, Rio, 2006.
    [Bibtex]