University of Lincoln

Gao, X., Xue, W., Lennox, C., Stevens, M., & Gao, J. (2024) Developing a hybrid convolutional neural network for automatic aphid counting in sugar beet fields, Computers and Electronics in Agriculture, 220, 108910.

Waltham, C., Perrett, A., Soni, R., & Fox, C. (2024) R4: rapid reproducible robotics research open hardware control system, arXiv pre-print.

Carter, S., Tsagkopoulos, N.C., Clawson, G., & Fox, C. (2023) OpenScout: Open Source Hardware Mobile Robot, Journal of Open Hardware, 7(1), pp. 9.

Heselden, J., & Das, G. (2023) Heuristics and Rescheduling in Prioritised Multi-Robot Path Planning: A Literature Review, Machines, 11(11), 1033.

Hurst, B., Bellotto, N., & Bosilj, P. (2023) An Assessment of Self-supervised Learning for Data Efficient Potato Instance Segmentation, Towards Autonomous Robotic Systems. TAROS 2023: Lecture Notes in Computer Science, 14136, pp. 267-278. Springer, Cham.

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Ravikanna, R., Heselden, J., Arshad Khan, M., Perrett, A., Zhu, Z., Das, G., & Hanheide, M. (2023) Smart Parking System Using Heuristic Optimization for Autonomous Transportation Robots in Agriculture. Towards Autonomous Robotic Systems. TAROS 2023: Lecture Notes in Computer Science, 14136, pp. 38-50. Springer, Cham.

Davy, J., & Fox, C. (2023) Simultaneous Base and Arm Trajectories for Multi-target Mobile Agri-Robot. Towards Autonomous Robotic Systems. TAROS 2023: Lecture Notes in Computer Science, 14136, pp. 214-226. Springer, Cham.

Vayakkattil, S., Cielniak, G. & Calisti, M. (2023) Plant Phenotyping Using DLT Method: Towards Retrieving the Delicate Features in a Dynamic Environment. Towards Autonomous Robotic Systems. TAROS 2023: Lecture Notes in Computer Science, 14136, pp. 3–14. Springer, Cham.

Nazari, K., Gandolfi, G., Talebpour, Z., Rajendran, V., Mandil, W., Rocco, P., & Ghalamzan-E., A (2023) Deep Functional Predictive Control (deep-FPC):
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Darbyshire, M., Sklar, E., & Parsons, S. (2023) Hierarchical Mask2Former: Panoptic Segmentation of Crops, Weeds and Leaves, Research Gate pre-print.

Le Louëdec, J., & Cielniak, G. (2023) Key Point-Based Orientation Estimation of Strawberries for Robotic Fruit Picking. Computer Vision Systems. ICVS 2023. Lecture Notes in Computer Science, 14253, pp. 148–158. Springer, Cham.

Wagener, N., & Cielniak, G. (2023) Vision-based Monitoring of the Short-term Dynamic Behaviour of Plants for Automated Phenotyping. Research Gate pre-print.

Mandil, W., Rajendran, V., Nazari, K., & Ghalamzan Esfahani, A. (2023) Tactile-Sensing Technologies: Trends, Challenges and Outlook in Agri-Food Manipulation. Sensors, 23(17), pp. 7362.

Cox, J., Tsagkopoulos Nikolaos., Rozsypálek, Z., Krajník, T., Sklar, E., & Hanheide, M. (2023) Visual teach and generalise (VTAG)—Exploiting perceptual aliasing for scalable autonomous robotic navigation in horticultural environments. Computers and Electronics in Agriculture, 212(2023), pp. 108054.

Rajendran S.V., Mandil, W., Parsons, S., & Ghalamzan Esfahani, A. (2023) Acoustic Soft Tactile Skin (AST Skin). arXiv:2303.17355 [cs.RO].

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Pearson, S., Brewer, S., Manning, L., Bidaut, L., Onoufriou, G., Durrant, A., Leontidis, G., Jabbour, C., Zisman, A., Parr, G., Frey, J., & Maull, R. (2023) Decarbonising our food systems: contextualising digitalisation for net zero. Frontiers in Sustainable Food Systems, 7(2023).

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Darbyshire, M., Salazar-Gomez, A., Lennox, C., Gao, J., Sklar, E., & Parsons, S. (2022). Localising Weeds Using a Prototype Weed Sprayer. UKRAS22 Conference “Robotics for Unconstrained Environments” Proceedings, 12-13.

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Mayoral Baños, J.C., From, P.J., & Cielniak, G. (2023). Towards Safe Robotic Agricultural Applications: Safe Navigation System Design for a Robotic Grass-Mowing Application through the Risk Management Method. Robotics, 12(3).

Craigon, P.J., Sacks, J., Brewer, S., Frey, J., Gutierrez, A., Jacobs, N., Kanza, S., Manning, L., Munday, S., Wintour, A., & Pearson, S., (2023). Ethics by design: Responsible research & innovation for AI in the food sector. Journal of Responsible Technology, 13(2023), pp. 100051.

Manning, L., Brewer, S., Craigon, P. , Frey, J., Gutierrez, A., Jacobs, N., Kanza, S., Munday, S., Sacks, J. and Pearson, S. (2023) Reflexive governance architectures: considering the ethical implications of autonomous technology adoption in food supply chains. Trends in Food Science & Technology, 133, pp. 114-126. ISSN 0924-2244

Qi, C., Sandroni, M., Westergaard, J.C., Høegh Riis Sundmark, E., Bagge, M., Alexandersson, E. and Gao, J. (2023) In-field classification of the asymptomatic biotrophic phase of potato late blight based on deep learning and proximal hyperspectral imaging. Computers and Electronics in Agriculture, 205. ISSN 01681699

Wichitwechkarn, V., and Fox, C. (2023) ‘MACARONS: A Modular and Open-Sourced Automation System for Vertical Farming‘. Journal of Open Hardware, 7 (1). ISSN 2514-1708.

Harman, H., and Sklar, E. (2022) Multi-agent Task Allocation for Fruit Picker Team Formation (Extended Abstract). In: The 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2022).

Clawson, G., &  Fox, C. (2022) Blockchain Crop Assurance and Localisation. UKRAS22 Conference “Robotics for Unconstrained Environments” Proceedings, 10-11.

Owen, A., Harman, H., & Sklar, E.(2022) Towards Autonomous Task Allocation Using a Robot Team in a Food Factory. UKRAS22 Conference “Robotics for Unconstrained Environments” Proceedings, 82-83.

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Harman, H., and Sklar, E. (2022) Multi-Agent Task Allocation Techniques for Harvest Team Formation. In: Advances in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection, 13th-15th July 2022, Italy.

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Choi, T., Would, O., Salazar-Gomez, A., and Cielniak, G. (2022) Self-supervised Representation Learning for Reliable Robotic Monitoring of Fruit Anomalies. In: 2022 IEEE International Conference on Robotics and Automation (ICRA), 23-27 May 2022, Philadelphia (PA), USA.

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Heiwolt, K., Duckett, T., &  Cielniak, G. (2021) Deep Semantic Segmentation of 3D Plant Point Clouds. Towards Autonomous Robotic Systems. TAROS 2021. Lecture Notes in Computer Science, vol.13054, pp. 36-45. Springer, Cham. 

Ravikanna, R., & Millard, A. (2021) Task Allocation with Manipulative Dynamic Auctioneering for Multi Robot Systems. UKRAS21 Conference: “Robotics at home” Proceedings, 45-46.

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Fentanes, J.P., Badiee, A., Duckett, T., Evans, J., Pearson, S., and Cielniak, G. (2020). Kriging based robotic exploration for soil moisture mapping using a cosmic ray sensor. Journal of Field Robotics, 37(1): 122–136. January 2020.

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Matsantonis, C., & Lasenby, J. (2023) A Geometric Algebra Solution to the 3D Registration Problem, Towards Autonomous Robotic Systems. TAROS 2023: Lecture Notes in Computer Science, 14136. Springer, Cham.

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Iida, F., Maiolina, P., Abdulali, A., & Wang, M. (2023) Towards Autonomous Robotic Systems: 24th Annual Conference, TAROS 2023, Cambridge, UK, September 13–15, 2023, Proceedings. Springer Nature.

Almanzor, E., Birell, S., & Iida, F. (2023) Rapid Development and Performance Evaluation of a Potato Planting Robot. Towards Autonomous Robotic Systems. TAROS 2023: Lecture Notes in Computer Science, 14136, pp. 15–25. Springer, Cham.

Shi, J., Abdulali, A., Sochacki, G., & Iida, F. (2023) Closed-Loop Robotic Cooking of Soups with Multi-modal Taste Feedback. Towards Autonomous Robotic Systems. TAROS 2023: Lecture Notes in Computer Science, 14136, pp. 51–62. Springer, Cham.

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Clawson, G. (2023) Applications of Distributed Ledger Technologies in Robotics. 2023 IEEE/SICE International Symposium on System Integration (SII).

Moncur, B., Galvez Trigo, M.J., & Mortara, L. (2023) Augmented Reality to Reduce Cognitive Load in Operational Decision-Making. Augmented Cognition. HCII 2023. Lecture Notes in Computer Science, vol. 14019, pp. 328-346. Springer, Cham.

Taylor, N.P., & Cunniffe, N.J. (2023) Coupling machine learning and epidemiological modelling to characterise optimal fungicide doses when fungicide resistance is partial or quantitative. Journal of the Royal Society Interface, 20(201), ISSN: 1742-5662.

Katiyar, S.A., Lee, L., Iida, F., & Nurzaman, S.G. (2023) Energy Harvesting for Robots with Adaptive Morphology. Soft Robotics, 10(2), pp. 365-379.

Clawson, G. (2023) A Technology Readiness Level for Blockchain. In: The 38th ACM/SIGAPP Symposium On Applied Computing, 27-31 March 2023, Tallinn Estonia.

Pietschmann, L., Bohné, T., & Tsapali, M. (2023) Extended Reality Visual Guidance for Industrial Environments: A Scoping Review. 2022 IEEE 3rd International Conference on Human-Machine Systems (ICHMS), Orlando, FL, USA, 2022, pp. 1-6.

Almanzor, E., Ye, F., Shi, J., Thuruthel, T.G., Wurdemann, H.A., Iida, F. (2023) Static Shape Control of Soft Continuum Robots Using Deep Visual Inverse Kinematic Models. IEEE Transactions on Robotics, 39(4), pp. 2973-2988.

Almanzor, E., Thuruthel, T.G., & Iida, F. (2022) Automated Fruit Quality Testing using an Electrical Impedance Tomography-Enabled Soft Robotic Gripper. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 2022, pp. 8500-8506.

Hughes, J., Abdulali, A., Hashem, R., & Iida, F. (2022) Embodied Artificial Intelligence: Enabling the Next Intelligence Revolution. IOP Conference Series: Materials Science and Engineering, 1261 012001.

Sochacki, G., Hughes, J., and Iida, F. (2022) Sensorized Compliant Robot Gripper for Estimating the Cooking Time of Boil-Cooked Vegetables. International Conference on Intelligent Autonomous Systems, 16, pp. 227-238.

Danno, D., Hauser, S., and Iida, F. (2022) Robotic cooking through pose extraction from human natural cooking using openpose. International Conference on Intelligent Autonomous Systems, 16, pp. 288-298.

Sochacki, G., Abdulali, A., and Iida, F. (2022) Mastication-Enhanced Taste-Based Classification of Multi-Ingredient Dishes for Robotic Cooking. Frontiers in Robotics and AI, 9, pp.108-121.

Thelander, M., Landberg, K., Muller, A., Cloarec, G., Cunniffe, N., Huguet, S., Soubigou-Taconnat, L., Brunaud, V., and Coudert, Y. (2022) Apical dominance control by TAR-YUC-mediated auxin biosynthesis is a deep homology of land plants. Current Biology July 2022.

Thuruthel, T.T., Iida, F. (2022) Multimodel Sensor Fusion for Learning Rich Models for Interacting Soft Robots. arXiv:2205.04202v1 [cs.RO].

Aubin, C.A., Gorissen, B., Milana, E., Buskohl, P.R., Lazarus, N., Slipher, G.A., Keplinger, C., Bongard, J., Iida, F., and Lewis, J.A. (2022) Towards enduring autonomous robots via embodied energy. Nature, 602, pp.393-402.

Sochacki, G., Abdulali, A., Cheke, L., and Iida, F.(2022) Theoretical Framework for Human-Like Robotic Taste with Reference to Nutritional Needs. IOP Conference Series: Materials Science and Engineering, 1292.

Wang, H., Thuruthel, T.G., Gilday, K., Abdulali, A., and Iida, F. (2022) Machine Learning for Soft Robot Sensing and Control: A Tutorial Study. 2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS), Coventry, United Kingdom, 2022, pp. 01-06.

Costi, L., Maiolino, P., and Iida, F. (2022) How the Environment Shapes Tactile Sensing: Understanding the Relationship between Tactile Filters and Surrounding Environment. Frontiers in Robotics and AI, July 2022, pp.180.

Bücker, C., Geissdoerfer, M., and Kumar, M. (2022) 100 practices to foster consumer acceptance in the circular economy. R&D Management Conference 2021: Innovation in an Era of Disruption. Glasgow, Scotland.

Chen, X., Lawrence, J.M., Wey, L.T., Schertel, L., Jing, Q., Vignolini, S., Howe, C.J., Kar-Narayan, S., and Zhang, J.Z. (2022) 3D-printed hierarchical pillar array electrodes for high-performance semi-artificial photosynthesis. Nature Materials, 21, pp.811-818.

Sadati, H., ElDiwiny, M., Nurzaman, S.G., Iida, F., and Nanayakkara, T. (2021) Embodied Intelligence & Morphological Computation in Soft Robotics Community: Collaborations, Coordination, and Perspective. Embodied Intelligence. IPO Conference Series: Materials Science and Engineering, 1261 01005.

Gielis, J., Shankar, A., and Prorok, A. (2022) A Critical Review of Communications in Multi-Robot Systems. arXiv:2206.09484 [cs.RO].

Ho, W.R., Tsolakis, N., Dawes, T., Dora, M., and Kumar, M. (2022) A Digital Strategy Development Framework for Supply Chains. IEEE Transactions on Engineering Management, 70(7), pp. 2493-2506, July 2023.

Thelander, M., Landberg, K., Muller, A.R.J., Cloarec, G., Cunniffe, N., Huguet, S., Soubigou-Taconnat, L., Brunaud, V., and Coudert, Y. (2022) Apical and basal auxin sources pattern shoot branching in a moss, Cold Spring Harbor Laboratory. bioRxiv.

Moencks, M., Roth, E., Bohné, T., Romero, D., and Stahre, J. (2022) Augmented Workforce Canvas: a management tool for guiding human-centric, value-driven human-technology integration in industry. Computers & Industrial Engineering, 163, ISSN 0360-8352.

Brion, D.A.J., Shen, M., and Pattinson, S.W. (2022) Automated recognition and correction of warp deformation in extrusion additive manufacturing. Additive Manufacturing. 56.

Pattinson, S., and Brion, D.A.J. (2022) Automated Recognition and Correction of Warp Deformation in Extrusion. Additive Manufacturing. Volume 56, August 2022, 102838.

Brintrup, A., Kosasih, E.E., MacCarthy, B.L., and Demirel, G. (2022) Digital supply chain surveillance: concepts, challenges, and frameworks. The Digital Supply Chain, pp.379-396.

Ogbeide, O., Bae, G., Yu, W., Morrin, E., Song, T., Song, W., Li, Y., Su, B., An, K., and Hasan, T. (2022) Inkjet‐Printed rGO/binary Metal Oxide Sensor for Predictive Gas Sensing in a Mixed Environment. Advanced Functional Materials, 32(25).

Prorok, A., Kumar, V., Sadler, B., and Sukhatme, G. (2022) Introduction to the Special Section on Resilience in Networked Robotic Systems. IEEE Transactions on Robotics, 38(1).

Sochacki, G., Hughes, J., & Iida, F. (2022) Sensorized Compliant Robot Gripper for Estimating the Cooking Time of Boil-Cooked Vegetables. In: Ang Jr, M.H., Asama, H., Lin, W., Foong, S. (eds) Intelligent Autonomous Systems 16. IAS 2021. Lecture Notes in Networks and Systems, vol 412. Springer, Cham.

Tsai, C., Ahn, J.M., and Mortara, L. (2022) Managing platform-based ecosystems in B2B markets–out-bound open innovation perspective. International Journal of Technology Management, 89(3-4), pp.139-162.

Calcagno, V., Cunniffe, N.J., Hamelin, F.M. (2022) Metacommunity dynamics and the detection of species associations in co‐occurrence analyses: Why patch disturbance matters. Functional Ecology, 36(6), pp.1483-1499.

Baikie, T.K., Wey, L.T., Medipally, H., Reisner, E., Nowaczyk, M.M., Friend, R.H., Howe, C.J., Schnedermann, C., Rao, A., and Zhang, J.Z. (2022) Photosynthesis re-wired on the pico-second timescale. arXiv:2201.13370 [physics.bio-ph].

Murray-Watson, R.E., and Cunniffe, N. (2022) Tolerant crops increase growers’ yields but promote selfishness: how the epidemiology of disease resistant and tolerant varieties affect grower behaviour. bioRxiv.

Kosasih, E.E., Margaroli, F., Gelli, S., Aziz, A., Wildgoose, N., and Brintrup, A. (2022) Towards knowledge graph reasoning for supply chain risk management using graph neural networks. International Journal of Production Research.

Prorok, A., Malencia, M., Carlone, L., Sukhatme, G.S., Sadler, B.M., & Kumar, V. (2021) Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systems. arXiv:2109.12343v1 [cs.RO].

Sochacki, G., Hughes, J., Hauser, S., & Iida, F. (2021) Closed-Loop Robotic Cooking of Scrambled Eggs with a Salinity-based ‘Taste’ Sensor. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic, 2021, pp. 594-600.

Balcerowicz, M., Shetty, K.N., & Jones, A.M. (2021) O auxin, where art thou? Nature Plants, 7, pp. 546–547.

Rizza, A., Tang, B., Stanley, C.E., Grossmann, G., Owen, M.R., Band, L.R., & Jones, A.M. (2021) Differential biosynthesis and cellular permeability explain longitudinal gibberellin gradients in growing roots. Proceedings of the National Academy of Sciences, 118(8).

Howard, T., Prorok, A., & Kress-Gazit, H. (2020) Guest Editorial: Robotics: Science and Systems 2018 (RSS 2018). Autonomous Robots, 44, pp. 1287-1288.

University of East Anglia

Bennett, J., & Finlayson, G.D. (2024) Simplifying Tone Curves for Image Enhancement, Pre-print.

Rogers, H., De La Iglesia, B., & Zebin, T. (2023) Evaluating the Use of Interpretable Quantized Convolutional Neural Networks for Resource-Constrained Deployment, Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 1, pp.109-120.

Gudelis, M., Mackiewicz, M., Bremner, J., Fielding, S. (2023) Computer Vision Pipeline for Automated Antarctic Krill Analysis, arXiv pre-print.

Rogers, H., De La Iglesia, B., Zebin, T., Cielniak, G., Magri, B. (2023). An Agricultural Precision Sprayer Deposit Identification System, 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE), Auckland, New Zealand, 2023, pp. 1-6.

Rogers, H., De La Iglesia, B., Zebin, T., Cielniak, G., Magri, B. (2023). An Automated Precision Spraying Evaluation System. Towards Autonomous Robotic Systems. TAROS 2023: Lecture Notes in Computer Science, 14136, pp. 26–37. Springer, Cham.

Hobley, B., Mackiewicz, M., Bremner, J., Dolphin, T., & Arosio, R. (2023) Crowdsourcing experiment and fully convolutional neural networks for coastal remote sensing of seagrass and macro-algae. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (Early Access), pp. 1-13.

Game, C.A., Thompson, M.B., & Finlayson, G.D. (2023) Weibull Tone Mapping (WTM) for the Enhancement of Underwater Imagery. Sensors 2023, 23(7), 3533.

Fearne, A., & Wagner, B. (2022) Value chain analysis: a diagnostic tool for building sustainable supply chains. In: Childe, S., & Anabela, S. (eds.) Handbook of Research Methods for Supply Chain Management. Edward Elgar Publishing, 18 Oct 2022. pp. 368.

Fisher, M., French, G., Gorpincenko, A., Holah, H., Clayton, L., Skirrow, R., & Mackiewicz, M. (2022) Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp. 349-361.

Didonet, S.R., & Fearne, A. (2022) The impact of market information use on entrepreneurial performance: insights from the UK food industry. Journal of Small Business and Enterprise Development. ISSN: 1462-6004.

Alkhudaydi, T., & De La Iglesia, B. (2022) Counting spikelets from infield wheat crop images using fully convolutional networks. Neural Computing and Applications, 34(20), pp. 17539-17560.

Alahamade, W., Lake, I., Reeves, C. E., & De La Iglesia, B. (2022) A multi-variate time series clustering approach based on intermediate fusion: A case study in air pollution data imputation. Neurocomputing, 490, pp. 229-245.

Fearne, A., Borzino, N., De La Iglesia, B., Moffatt, P., & Robbins, M. (2022) Using supermarket loyalty card data to measure the differential impact of the UK soft drink sugar tax on buyer behaviour. Journal of Agricultural Economics, 73(2), pp. 321-337.

Deeb, R., & Finlayson, G. D. (2022) Locus filters. Optics Express, 30(8), pp. 12902-12917.

Alahamade, W., Lake, I., Reeves, C., & De La Iglesia, B. (2021) Evaluation of multi-variate time series clustering for imputation of air pollution data. Geoscientific Instrumentation, Methods and Data Systems, 10, pp. 265-285.

Game, C. A., Thompson, M. B., & Finlayson, G. D. (2021) Chromatic Weibull Tone Mapping for Underwater Image Enhancement. Proceedings of the International Colour Association Congress, pp. 239-244.

Hobley, B., Arosio, R., French, G., Bremner, J., Dolphin, T. and Mackiewicz, M. (2021). Semi-Supervised Segmentation for Coastal Monitoring Seagrass Using RPA Imagery. Remote Sensing, 13 (9). ISSN 2072-4292

Gorpincenko, A., French, G., Knight, P., Challis, M., & Mackiewicz, M. (2021) Improving automated sonar video analysis to notify about jellyfish blooms. IEEE Sensors Journal, 21(4), pp. 4981-4988.

Blackwell, R.E., Harvey, R., Queste, B.Y. and Fielding, S. (2020). Colour maps for fisheries acoustic echograms. ICES Journal of Marine Science, 77 (2). 826–834. ISSN 1054-3139

Occhibove, F., Chapman, D.S., Mastin, A.J., Parnell, S.S.R., Agstner, B., Mato-Amboage, R., Jones, G., Dunn, M., Pollard, C.R.J., Robinson, J.S., Marzano, M., Davies, A.L., White, R.M., Fearne, A., and White, S.M. (2020). Eco-epidemiological uncertainties of emerging plant diseases: The challenge of predicting Xylella fastidiosa dynamics in novel environments. Phytopathology, 110(11): 1740–1750. November 2020.

French, G., Mackiewicz, M., Fisher, M., Holah, H., Kilburn, R., Campbell, N. and Needle, C. (2020). Deep neural networks for analysis of fisheries surveillance video and automated monitoring of fish discards. ICES Journal of Marine Science, 77 (4). 1340–1353. ISSN 1054-3139

Colmer, J., O’Neill, C.M., Wells, R., Bostrom, A., Reynolds, D., Websdale, D., Shiralagi, G., Lu, W., Lou, Q., Cornu, T.L., Ball, J., Renema, J., Andaluz, G.F., Benjamins, R., Penfield, S., and Zhou, J. (2020). SeedGerm: a cost effective phenotyping platform for automated seed imaging and machine learning based phenotypic analysis of crop seed germination. New Phytologist, 228(2): 778–793. October 2020.

Bauer, A., Bostrom, A.G., Ball, J., Applegate, C., Cheng, T., Laycock, S., Rojas, S.M., Kirwan, J. and Zhou, J. (2019). Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production: AirSurf-Lettuce. Horticulture Research, 2019 (6). pp. 1-12. ISSN 2052-7276

Alkhudaydi, T., Reynolds, D., Griffiths, S., Zhou, J. and De La Iglesia, B. (2019). An Exploration of Deep-Learning Based Phenotypic Analysis to Detect Spike Regions in Field Conditions for UK Bread Wheat. Plant Phenomics, 2019 (July). pp. 1-17. ISSN 2643-6515

University of Lincoln

Kucner, T.P., Magnusson, M., Mghames, S., Palmieri, L., Verdoja, F., Swaminathan, C.S., Krajník, T., Schaffernicht, E., Bellotto, N., Hanheide, M., & Lilienthal, A.J. (2023) Survey of maps of dynamics for mobile robots, The International Journal of Robotics Research, 2023(0).

Seemakurthy, K., Bosilj, P., Aptoula, E., & Fox, C. (2023) Domain Generalised Fully Convolutional One Stage Detection. 2023 IEEE International Conference on Robotics and Automation (ICRA), London, United Kingdom, 2023, pp. 7002-7009.

Mahesar, Q., & Parsons, S. (2023) Argument Schemes and a Dialogue System for Explainable Planning. ACM Transactions on Intelligent Systems and Technology, accepted July 2023.

Del Duchetto, F., Kucukyilmaz, A., & Hanheide, M. (2023). In-the-Wild Failures in a Long-Term HRI Deployment. Workshop on Robot Execution Failures and Failure Management Strategies at IEEE ICRA 2023, June 2nd, 2023, ExCeL London.

Zhu, Z., Das, G., & Hanheide, M. (2023) Autonomous Topological Optimisation for Multi-robot Systems in Logistics. Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing (SAC ’23). Association for Computing Machinery, New York, NY, USA, pp. 791–799.

Seemakurthy, K., Fox, C., Aptoula, E., & Bosilj, P. (2023) Domain Generalised Faster R-CNN. The 37th AAAI conference on Artificial Intelligence, 7th Feb 2023 to 14th Feb 2023, Walter E Convention Centre, Washington DC.

Astolfi, A., & Calisti, M. (2023) Articulated legs allow energy optimization across different speeds for legged robots with elastically suspended loads. 2023 IEEE International Conference on Soft Robotics (RoboSoft), Singapore, Singapore, 2023, pp. 1-7.

Sheikh Sofla, M., Vayakkattil, S., & Calisti, M. (2023) Spatial Position Estimation of Lightweight and Delicate Objects using a Soft haptic Probe. 2023 IEEE International Conference on Soft Robotics (RoboSoft), Singapore, Singapore, 2023, pp. 1-6.

Picardi, G., Astolfi, A., Chatzievangelou, D., Aguzzi, J., & Calisti, M. (2023) Underwater legged robotics: review and perspectives. Bioinspiration & Biomimetics, 18(3), pp. 031001.

Castri, L., Mghames, S., Hanheide, M., and Bellotto, N. (2023) ‘Enhancing Casual Discovery from Robot Sensor Data in Dynamic Scenarios‘. In: Conference on Casual Learning and Reasoning (CLeaR), 11-14 April 2023, Tübingen, Germany.

Nazari, K., Mandil, W. and Ghalamzan Esfahani, A. (2022) Proactive slip control by learned slip model and trajectory adaptation. In: 6th Conference on Robot Learning, 14th-16th December 2022, Auckland, New Zealand.

Del Duchetto, F., and Hanheide, M. (2022) Learning on the Job: Long-Term Behavioural Adaptation in Human-Robot Interactions. IEEE Robotics and Automation Letters, 7 (3). pp. 6934-6941. ISSN 2377-3766.

Bennett, J., Moncur, B., Fogarty, K., Clawson, G., and Fox, C. (2022) Towards Open Source Hardware Robotic Woodwind: an Internal Duct Flute Player. In: International Computer Music Conference, 3-7 July 2022, Limerick, Ireland.

Wang, N., Das, G., & Millard, A. (2022) Learning Cooperative Behaviours in Adversarial Multi-agent Systems. Towards Autonomous Robotic Systems. TAROS 2022. Lecture Notes in Computer Science, vol. 13546, pp. 179-189. Springer, Cham.

Le Louedec, J., and Cielniak, G. (2021) Gaussian map predictions for 3D surface feature localisation and counting. In: BMVC.

Ghidoni, S., Terreran, M., Evangelista, D., Menegatti, E., Eitzinger, C., Villagrossi, E., Pedrocchi, N., Castaman, N., Malecha, M., Mghames, S., Castri, L., Hanheide, M., and Bellotto, N. (2022) From Human Perception and Action Recognition to Causal Understanding of Human-Robot Interaction in Industrial Environments. In: Ital-IA 2022, 9th-11th February 2022, Online.

Lei, F., Peng, Z., Liu, M., Peng, J., Cutsuridis, V., and Yue, S. (2022) A Robust Visual System for Looming Cue Detection Against Translating Motion. IEEE Transactions on Neural Networks and Learning Systems . pp. 1-15. ISSN 2162-237X

Pignon, C.P., Fernandes, S.B., Valluru, R., Bandillo, N., Lozano, R., Buckler, E., Gore, M.A., Long, S.P., Brown, P.J., and Leakey, A.D.B. (2021). Phenotyping stomatal closure by thermal imaging for GWAS and TWAS of water use efficiency-related genes. Plant Physiology. August 2021.

Yang, F., Shu, L., Yang, Y., Han, G., Pearson, S., and Li, K. (2021). Optimal Deployment of Solar Insecticidal Lamps over Constrained Locations in Mixed-Crop Farmlands. IEEE Internet of Things Journal. March 2021.

Bochtis, D., Benos, L., Lampridi, M., Marinoudi, V., Pearson, S., and Sorensen, C.G. (2020). Agricultural Workforce Crisis in Light of the COVID-19 Pandemic. Sustainability, 12(19): 8212. October 2020.

University of Cambridge

Langtry, M., Wichitwechkarn, V., Ward, R., Zhuang, C., Kreitmair, M.J., Makasis, N., Conti, Z.X., Choudhary, R. (2024) Impact of data usage for forecasting on performance of model predictive control in buildings with smart energy storage, arXiv pre-print.

Potdar, P., Hardam, D., Almanzor, E., & Iida, F. (2024) High-Speed Tactile Braille Reading via Biomimetic Sliding Interactions, IEEE Robotics and Automation Letters, 9(3).

Schoepf, S., Foster, J., & Brintrup, A. (2024) Parameter-tuning-free data entry error unlearning with adaptive selective synaptic dampening, arXiv pre-print.

Schoepf, S., Foster, J., & Brintrup, A. (2023) Identifying contributors to supply chain outcomes in a multi-echelon setting: a decentralised approach, arXiv pre-print.

Zuercher, P.J. (2023) Evaluating hardware differences for crowdsourcing and traditional recruiting methods, arXiv pre-print.

Ye, F., Abdulali, A. & Iida, F. (2023) Simulation of Collective Bernoulli-Ball System for Characterizing Dynamic Self-stability. Towards Autonomous Robotic Systems. TAROS 2023: Lecture Notes in Computer Science, 14136, pp. 367-378. Springer, Cham.

Georgopoulou, A., Hardman, D., Thuruthel, T.G., Iida, F., & Clemens, F. (2023) Sensorized Skin With Biomimetic Tactility Features Based on Artificial Cross-Talk of Bimodal Resistive Sensory Inputs. Advanced Science, Early View.

Wang, H., Zhang, Y., & Iida, F. (2023) Reduced-Order Modeling of a Soft Anthropomorphic Finger for Piano Keystrokes. Towards Autonomous Robotic Systems. TAROS 2023: Lecture Notes in Computer Science, 14136, pp. 405-416. Springer, Cham.

Wang, H., Nonaka, T., Abdulali, A. & Iida, F. (2023) Coordinating upper limbs for octave playing on the piano via neuro-musculoskeletal modeling. Bioinspiration & Biomimetics. IOP Publishing Ltd.

Pietschmann, L., Zuercher, P.D., Bubik, E., Chen, Z., Pfister, H., & Bohné, T. (2023) Quantifying the Impact of XR Visual Guidance on User Performance Using a Large-Scale Virtual Assembly Experiment. Research Gate Pre-print.

Zhong, F., Fogarty, F., Hanji. P., Wu, T., Sztrajman, A., Speilberg, A., Tagliasacchi, A., Bosilj, P., & Oztireli, A. (2022). Neural Fields with Hard Constraints of Arbitrary Differential Order. arXiv:2306.08943 pre-print [cs.LG].

Costi, L., & Iida, F. (2023) Multi-silicone Bilateral Soft Physical Twin as an Alternative to Traditional User Interfaces for Remote Palpation: a Comparative Study. Research Square Pre-Print.

Costi, L., Almanzor, E., Scimeca, L., & Iida, F. (2023) Comparative Study of Hand-Tracking and Traditional Control Interfaces for Remote Palpation. Towards Autonomous Robotic Systems. TAROS 2023: Lecture Notes in Computer Science, 14136, pp. 457–469. Springer, Cham.

Anvo, N.R., Thuruthel, T.G., Taha, H.M., de Silva, L., Al-Tabbaa, A., Brilakis, I., & Iida, F. (2023) Automated 3D Mapping, Localization and Pavement Inspection with Low Cost RGB-D Cameras and IMUs. Towards Autonomous Robotic Systems. TAROS 2023. Lecture Notes in Computer Science, vol 14136, pp. 279–291. Springer, Cham.

Hardman, D., George Thuruthel, T., and Iida, F. (2022) Manipulation of free-floating objects using Faraday flows and deep reinforcement learning. Scientific reports, 12(335).

Gilday, K., Hughes, J., and Iida, F. (2022) Sensing, Actuating, and Interacting Through Passive Body Dynamics: A Framework for Soft Robotic Hand Design. Soft Robotics.

Scimeca, L., Hughes, Josie; M.P., He, L., Nanayakkara, T., and Iida, F. (2022) Action augmentation of tactile perception for soft-body palpation. Soft robotics, 9(2), pp.280-292.

Costi, L., Tagliabue, A., Maiolino, P., Clemens, F., and Iida, F. (2022) Magneto-Active Elastomer Filter for Tactile Sensing Augmentation Through Online Adaptive Stiffening. IEEE Robotics and Automation Letters, 7(3), pp.5928-5933.

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Howison, T., Hughes, J., and Iida, F. (2022) Morphological Sensitivity and Falling Behavior of Paper V-Shapes. Artificial Life, 27(3-4), pp.204-219.

Roels, E., Terryn, S., Iida, F., Bosman, A.W., Norvez, S., Clemens, F., Van Assche, G., Vanderborght, B., and Brancart, J. (2022) Processing of Self‐Healing Polymers for Soft Robotics. Advanced Materials, 34(1).

George Thuruthel, T., Gardner, P., and Iida, F. (2022) Closing the Control Loop with Time-Variant Embedded Soft Sensors and Recurrent Neural Networks. Soft Robotics, Apr 2022.

Hashem, R., and Iida, F. (2022) Embedded Soft Sensing in Soft Ring Actuator for Aiding with the Self-Organisation of the In-Hand Rotational Manipulation. 2022 IEEE 5th International Conference on Soft Robotics (RoboSoft), 2022, pp.498-503.

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Petchrompo, S., Coit, D.W., Brintrup, A., Wannakrairot, A., and Parlikad, A.K. (2022) A review of Pareto pruning methods for multi-objective optimization, Computers & Industrial Engineering.

Raymond, A., Malencia, M., Paulino-Passos, G., Prorok, A. (2022) Agree to disagree: Subjective fairness in privacy-restricted decentralised conflict resolution. Frontiers in Robotics and AI, 9.

Almanzor, E., Anvo, N.R., Thuruthel, T.G., & Iida, F. (2022) Autonomous detection and sorting of litter using deep learning and soft robotic grippers. Frontiers in Robotics and AI, 9:1064853. 

Che, W., Chaffey, T., and Forni, F. (2022) Analog cross coupled controller for oscillations: modeling and design via dominant system theory.

Yong, B.X., and Brintrup, A. (2022) Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection.

Zaplana, I., Hadfield, H., and Lasenby, J. (2022) Closed-form solutions for the inverse kinematics of serial robots using conformal geometric algebra. Mechanism and Machine Theory, 173.

Yong, B.X., and Brintrup, A. (2022) Coalitional Bayesian autoencoders: Towards explainable unsupervised deep learning with applications to condition monitoring under covariate shift. Applied Soft Computing, 123.

Sheng, Y., Liu, Y., Zhang, J., Yin, W., Oztireli, A.C., Zhang, H., Lin, Z., Shechtman, E., and Benes, B. (2022) Controllable Shadow Generation Using Pixel Height Maps.

Wu, T., Zhong, F., Tagliasacchi, A., Cole, F., and Oztireli, C. (2022) D $^ 2$ NeRF: Self-Supervised Decoupling of Dynamic and Static Objects from a Monocular Video.

Miranda-Villatoro, F.A., Forni, F., and Sepulchre, R.J. (2022) Dissipativity analysis of negative resistance circuits. Automatica, 136.

Yong, B.X., and Brintrup, A. (2022) Do autoencoders need a bottleneck for anomaly detection?.

Churamani, N., Kara, O., and Gunes, H. (2022) Domain-incremental continual learning for mitigating bias in facial expression and action unit recognition. IEEE Transactions on Affective Computing.

Xue, F., Budvytis, I., Reino, D.O., and Cipolla, R. (2022) Efficient Large-scale Localization by Global Instance Recognition. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.17348-17357.

Wang, H., Kaddour, J., Liu, S., Tang, J., Kusner, M., Lasenby, J., and Liu, Q. (2022) Evaluating Self-Supervised Learning for Molecular Graph Embeddings.

Bidinger, S.L., Han, S., Malliaras, G.G., and Hasan, T. (2022) Highly stable PEDOT: PSS electrochemical transistors. Applied Physics Letters, 120(7).

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Bae, G., Budvytis, I., and Cipolla, R. (2022) Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.2842-2851.

Paletta, Q., Arbod, G., and Lasenby, J. (2022) Omnivision forecasting: combining satellite observations with sky images for improved intra-hour solar energy predictions.

Zuercher, P.D., Bohné, T., Eger, V.M., and Mueller, F. (2022) Optimising virtual reality training in industry using crowdsourcing. Proceedings of the 12th Conference on Learning Factories (CLF 2022).

Dodik, A., Papas, M., Öztireli, C., and Müller, T. (2022) Path Guiding Using Spatio‐Directional Mixture Models. Computer Graphics Forum, 41(1), pp.172-189.

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Zhuang, C., Choudhary, R., and Mavrogianni, A. (2022) Probabilistic occupancy forecasting for risk-aware optimal ventilation through autoencoder Bayesian deep neural networks. Building and Environment.

Kortvelesy, R., and Prorok, A. (2022) QGNN: Value Function Factorisation with Graph Neural Networks

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Gama, F., Li, Q., Tolstaya, E., Prorok, A., and Ribeiro, A. (2022) Synthesizing decentralized controllers with graph neural networks and imitation learning. IEEE Transactions on Signal Processing, 70, pp.1932-1946.

Stoychev, S., and Gunes, H. (2022) The effect of model compression on fairness in facial expression recognition

Proselkov, Y., Herrera, M., Hernandez, M.P., Parlikad, A.K., and Brintrup, A. (2022) The value of information for dynamic decentralised criticality computation. IFAC-PapersOnLine, 55(2), pp.408-413.

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Lin, Y., & Finlayson, G.D. (2023) A Rehabilitation of Pixel-Based Spectral Reconstruction from RGB Images. Sensors 2023, 23(8), 4155.

Gorpincenko, A., & Mackiewicz, M. (2023) Extending Temporal Data Augmentation for Video Action Recognition. Image and Vision Computing. IVCNZ 2022. Lecture Notes in Computer Science, vol 13836. Springer, Cham.

Aleryani, A., Bostrom, A., Wang, W., &De La Iglesia, B. (2023) Multiple Imputation Ensembles for Time Series (MIE-TS). ACM Transactions on Knowledge Discovery from Data, 17(3), pp. 1–28.

Lin, Y., & Finlayson, G.D. (2023) An investigation on worst-case spectral reconstruction from RGB images via Radiance Mondrian World assumption. Color: Research and Application, 48(2), pp. 230-242.

Finlayson, G.D., & McVey, J. (2022) TM-Net: A Neural Net Architecture for Tone Mapping. Journal of Imaging 2022, 8(12), pp. 325.

Lin, Y., & Finlayson, G.D. (2022) Evaluating the Performance of Different Cameras for Spectral Reconstruction. Color and Imaging Conference, 30(1), pp. 213-218.

Zhou, H., Greenwood, D., Taylor, S., & Mackiewicz, M. (2022) Self-distillation and uncertainty boosting self-supervised monocular depth estimation. The 33rd British Machine Vision Conference Proceedings, 2022.

Busatto, R., & Harvey, R. (2022) Outdoor Navigation Assistants for Visually Impaired Persons: Problems and Challenges. Journal on Technology & Persons with Disabilities, 10, pp. 184-205.

Zhu, Y., & Finlayson, G. D. (2022) Matched illumination: Using light modulation as a proxy for a color filter that makes a camera more colorimetric. Optics Express, 30(12), pp. 22006-22024.

Alyahyan, S., & Wang, W. (2022) Decision level ensemble method for classifying multi-media data. Wireless Networks, 28(3), pp. 1219-1227.

French, G., & Mackiewicz, M. (2022) Colour augmentation for improved semi-supervised semantic segmentation. Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (4), pp. 356-363.

Wood, J., & Wang, W. (2022) Creating Variant Features to Enhance Covid-19 Predictions with Machine Learning Ensemble.

Lin, Y., & Finlayson, G. D. (2021) Investigating the upper-bound performance of sparse-coding-based spectral reconstruction from RGB images. Color and Imaging Conference, 2021(29), pp. 19-24.

Zhu, Y., & Finlayson, G. D. (2021) Designing a Color Filter with High Overall Transmittance for Improving the Color Accuracy of Digital Cameras. 29th Color and Imaging Conference – Color Science and Engineering Systems, Technologies, and Applications, CIC 2021 – Proceedings, of Final Program and Proceedings – IS and T/SID Color Imaging Conference, pp. 1-6.

Mcvey, J., & Finlayson, G. D. (2021) Towards a Generic Neural Network Architecture for Approximating Tone Mapping Algorithms. Proceedings of the IS&T London Imaging Meeting 2021: Imaging for Deep Learning, pages 93-96.

Hobley, B., Finlayson, G. D., Mackiewicz, M., Bremner, J., Dolphin, T., & Arosio, R. (2021) Improving image registration using colour transfer methods in remote sensing applications. The Congress of the International Color Association, pp. 299-304.

Lin, Y., & Finlayson, G. D. (2021) On the optimization of regression-based spectral reconstruction. Sensors, 21(16).

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Deeb, R., & Finlayson, G. D. (2021) The Locus Filter. The Congress of the International Color Association.

Lin, Y., & Finlayson, G. D. (2021) Recovering real-world spectra from RGB images under radiance mondrian-world assumption.

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