2023
1. N. Michlo, R. Klein, S. James Overlooked implications of the reconstruction loss for VAE disentanglement. Proceedings of the Thirty-second International Joint Conference on Artificial Intelligence, August 2023. |
2. D. Jarvis, V. Klar, R. Klein, B. Rosman, A. Saxe. Revisiting the Role of Relearning in Semantic Dementia. Conference on Cognitive Computational Neuroscience, August 2023. |
3. M. Nasir, M. Beukman, S. James, C. Cleghorn. Augmentative Topology Agents for Open-ended Learning. Genetic and Evolutionary Computation Conference, July 2023. |
4. D. Jarvis, R. Klein, B. Rosman, A. Saxe. On The Specialization of Neural Modules. Proceedings of the Eleventh International Conference on Learning Representations, May 2023. |
5. S. Singh, B. Rosman. The Challenge of Redundancy on Multi-agent Value Factorisation. International Conference on Autonomous Agents and Multiagent Systems, May 2023. [Extended Abstract] |
6. R. Lastrucci, I. Dzingirai, J. Rajab, A. Madodonga, M. Shingange, D. Njini, V. Marivate. Preparing the Vuk’uzenzele and ZA-gov-multilingual South African multilingual corpora. Workshop on Resources for African Indigenous Language (RAIL) @ EACL, May 2023. |
7. C. Currin, M. Asiedu, C. Fourie, B. Rosman, H. Turki, A. Tonja, J. Abbott, M. Ajala, S. Adedayo, C. Emezue, D. Machangara. A Framework for Grassroots Research Collaboration in Machine Learning and Global Health. ICLR Workshop on Machine Learning & Global Health, May 2023. |
8. O. Can Görür , B. Rosman , F. Sivrikaya , S. Albayrak. FABRIC: A Framework for the Design and Evaluation of Collaborative Robots with Extended Human Adaptation. ACM Transactions on Human-Robot Interaction, March 2023. |
2022
1. V. Cohen*, G. Nangue Tasse*, N. Gopalan, S. James, R. Mooney, B. Rosman. End-to-End Learning to Follow Language Instructions with Compositional Policies. Workshop on Language and Robot Learning @ CORL, December 2022. |
2. L.J. Arendse, B. Ingram, B. Rosman. Real Time In-Game Playstyle Classification Using A Hybrid Probabilistic Supervised Learning Approach. The Third Southern African Conference for AI Research Proceedings. Part of the book series: Communications in Computer and Information Science, Springer, December 2022. |
3. H. Combrink, V. Marivate, B. Rosman. Reinforcement Learning in Education: A Multi-Armed Bandit Approach. 5th EAI International Conference on Emerging Technologies for Developing Countries, December 2022. |
4. W. Onyothi Nekoto, J. Kreutzer, J. Rajab, M. Ochieng, J. Abbott. Participatory Translations of Oshiwambo: Towards Culture Preservation with Language Technology. Workshop on NLP for Positive Impact @ EMNLP, December 2022. |
5. H. Combrink, V. Marivate, B. Rosman. Comparing Synthetic Tabular Data Generation Between a Probabilistic Model and a Deep Learning Model for Education Use Cases. The Third Southern African Conference for AI Research Proceedings, December 2022. |
6. G. Crafford, B. Rosman. Improving Reinforcement Learning with Ensembles of Different Learners. RAPDASA/ROBMECH/PRASA/CoSAAMI International Conference, November 2022. |
7. G. Nangue Tasse, D. Jarvis, S. James, B. Rosman. Skill Machines: Temporal Logic Composition in Reinforcement Learning. Lifelong Learning of High-level Cognitive and Reasoning Skills Workshop @ IROS, October 2022. |
8. M. Nasir, M. Beukman, S. James, C. Cleghorn. Augmentative Topology Agents For Open-ended Learning. Lifelong Learning of High-level Cognitive and Reasoning Skills Workshop @ IROS, October 2022. |
9. T. Love*, D. Jarvis*, G. Nangue Tasse*, B. Ingram, S. James, B. Rosman. Facilitating Safe Sim-to-Real through Simulator Abstraction and Zero-shot Task Composition. Lifelong Learning of High-level Cognitive and Reasoning Skills Workshop @ IROS, October 2022. |
10. T. Love, R. Ajoodha, B. Rosman. Who should I trust? Cautiously learning with unreliable experts. Neural Computing and Applications, September 2022. |
11. L. Dunbar, B. Rosman, A. Cohn, M. Leonetti. Reducing the Planning Horizon through Reinforcement Learning. Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, September 2022. |
12. B. Ingram, B. Rosman, C. van Alten, R. Klein. Play-style Identification through Deep Unsupervised Clustering of Trajectories. Proceedings of the IEEE Conference on Games, August 2022. [Best Paper Nominee] |
13. B. Ingram, B. Rosman, C. van Alten, R. Klein. Improved Action Prediction through Multiple Model Processing of Player Trajectories. Proceedings of the IEEE Conference on Games, August 2022. |
14. K. Tessera, C. Matowe, A. Pretorius, B. Rosman, S. Hooker. Just-in-Time Sparsity: Learning Dynamic Sparsity Schedules. Workshop on Dynamic Neural Networks @ ICML, July 2022. |
15. M. Beukman, C. Cleghorn, S. James. Procedural Content Generation using Neuroevolution and Novelty Search for Diverse Video Game Levels. Proceedings of the Genetic and Evolutionary Computation Conference, July 2022. |
16. M. Vogt, B. Rosman. Analyzing Reinforcement Learning Algorithms for Nitrogen Fertilizer Management in Simulated Crop Growth. Proceedings of 43rd Conference of the South African Institute of Computer Scientists and Information Technologists, July 2022. [Best Paper Award] |
17. N. Muir, S. James. Combining Evolutionary Search with Behaviour Cloning for Procedurally Generated Content. Proceedings of 43rd Conference of the South African Institute of Computer Scientists and Information Technologists, July 2022. |
18. G. Nangue Tasse, D. Jarvis, S. James, B. Rosman. Skill Machines: Temporal Logic Composition in Reinforcement Learning. Technical Report, June 2022. |
19. T. Love, R. Ajoodha, B. Rosman. Harnessing the Wisdom of an Unreliable Crowd for Autonomous Decision Making. The Multi-disciplinary Conference on Reinforcement Learning and Decision Making, June 2022. [Extended Abstract] |
20. M. Beukman, M. Mitchley, D. Wookey, S. James, G. Konidaris. Adaptive Online Value Function Approximation with Wavelets. The Multi-disciplinary Conference on Reinforcement Learning and Decision Making, June 2022. [Extended Abstract] |
21. N. Michlo, D. Jarvis, R. Klein, S. James. Accounting for the Sequential Nature of States to Learn Representations in Reinforcement Learning. The Multi-disciplinary Conference on Reinforcement Learning and Decision Making, June 2022. [Extended Abstract] |
22. S. James, B. Rosman, G. Konidaris. Learning Abstract and Transferable Representations for Planning. The Multi-disciplinary Conference on Reinforcement Learning and Decision Making, June 2022. [Extended Abstract] |
23. J.J. Shipton, B. Rosman. Diverse Partner Creation with Partner Prediction for Robust K-Level Reasoning. The Multi-disciplinary Conference on Reinforcement Learning and Decision Making, June 2022. [Extended Abstract] |
24. G. Nangue Tasse, S. James, B. Rosman. World Value Functions: Knowledge Representation for Multitask Reinforcement Learning. The Multi-disciplinary Conference on Reinforcement Learning and Decision Making, June 2022. [Extended Abstract] |
25. G. Nangue Tasse, S. James, B. Rosman. Generalisation in Lifelong Reinforcement Learning through Logical Composition. Proceedings of the Tenth International Conference on Learning Representations, April 2022. |
26. S. James, B. Rosman, G. Konidaris. Autonomous Learning of Object-Centric Abstractions for High-Level Planning. Proceedings of the Tenth International Conference on Learning Representations, April 2022. |
27. M. Beukman. Analysing the Effects of Transfer Learning on Low-Resourced Named Entity Recognition Performance. 3rd Workshop on African Natural Language Processing, April 2022. |
28. J. Rajab. Effect of Tokenisation Strategies for Low-Resourced Southern African Languages. 3rd Workshop on African Natural Language Processing, April 2022. |
29. N. Kooverjee, S. James, T. van Zyl. Investigating Transfer Learning in Graph Neural Networks. Electronics, April 2022. |
2021
1. S. Singh, B. Rosman. The Challenge of Redundancy on Multi-Agent Value Factorisation. NeurIPS Workshop on Cooperative AI, December 2021. |
2. G. Nangue Tasse, S. James, B. Rosman. Generalisation in Lifelong Reinforcement Learning through Logical Composition. NeurIPS Deep Reinforcement Learning Workshop, December 2021. |
3. R. Sefala, T. Gebru, N. Moorosi, R. Klein. Constructing a Visual Dataset to Study the Effects of Spatial Apartheid in South Africa. NeurIPS 2021 Datasets and Benchmarks Track, December 2021. |
4. D. Poulton, R. Klein. Improving Pose Estimation through Contextual Activity Fusion. Southern African Conference for Artificial Intelligence Research, December 2021. |
5. R. Ajoodha, B. Rosman. Using Score-based Structure Learning to Computationally Learn Direct Influence between Hierarchical Dynamic Bayesian Networks. IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), December 2021. |
6. C. Gevaert, M. Carman, B. Rosman, Y. Georgiadou, R. Soden. Fairness and accountability of AI in disaster risk management: Opportunities and challenges. Patterns (Elsevier), November 2021. |
7. J. Harris-Dewey, R. Klein. Generative Adversarial Networks for Global Illumination and Indirect Lighting as a Replacement for Ray-tracing in Older GPU Hardware. International Conference in Soft Computing and Machine Intelligence, November 2021. |
8. V. Cohen*, G. Nangue Tasse*, N. Gopalan, S. James, M. Gombolay, B. Rosman. Learning to Follow Language Instructions with Compositional Policies. AAAI Fall Symposium on AI for Human-Robot Interaction, November 2021. |
9. A. Pantanowitz, E.Cohen, P. Gradidge, N. Crowther, V. Aharonson, B. Rosman, D. Rubin. Estimation of Body Mass Index from photographs using deep Convolutional Neural Networks. Informatics in Medicine Unlocked, September 2021. |
10. A. Pantanowitz, B. Rosman, N. Crowther, D. Rubin. The Hospital as a Sorting Machine. Informatics in Medicine Unlocked, August 2021. |
11. T. Love, R. Ajoodha, B. Rosman. Should I Trust You? Incorporating Unreliable Expert Advice in Human-Agent Interaction. Workshop on Human-aligned Reinforcement Learning for Autonomous Agents and Robots at ICDL, August 2021. |
12. K. Tessera, S. Hooker, B. Rosman. Keep the Gradients Flowing: Using Gradient Flow to Study Sparse Network Optimization. Sparsity in Neural Networks: Advancing Understanding and Practice, July 2021. |
13. L. Pratt, D. Govender, R. Klein. Defect Detection and Quantification in Electroluminescence Images of Solar PV Modules using U-net Semantic Segmentation. Renewable Energy, June 2021. |
14. M. Saeed, M. Nagdi, B. Rosman, H. Ali. Deep Reinforcement Learning for Robotic Hand Manipulation. International Conference on Computer, Control, Electrical, and Electronics Engineering, February 2021. |
15. M. Omer, R. Ahmed, B. Rosman, S. Babikir. Model Predictive-Actor Critic Reinforcement Learning for Dexterous Manipulation. International Conference on Computer, Control, Electrical, and Electronics Engineering, February 2021. |
16. H. Combrink, V. Marivate, B. Rosman. A Framework for Undergraduate Data Collection Strategies for Student Support Recommendation Systems in Higher Education. Southern African Conference for Artificial Intelligence Research, February 2021. |
17. T. Boloka, N. Makondo, B. Rosman. Knowledge Transfer using Model-Based Deep Reinforcement Learning. SAUPEC/ROBMECH/PRASA International Conference, January 2021. |
2020
1. G. Nangue Tasse, S. James, B. Rosman. A Boolean Task Algebra for Reinforcement Learning. Advances in Neural Information Processing Systems (NeurIPS), December 2020. |
2. G. Singh, C. Reynolds, M. Byrne, B. Rosman. A Remote Sensing Method to Monitor Water, Aquatic Vegetation, and Invasive Water Hyacinth at National Extents. Remote Sensing 2020, 12(24), 4021. |
3. O. Marom, B. Rosman. Utilising Uncertainty for Efficient Learning of Likely-Admissible Heuristics. International Conference on Automated Planning and Scheduling (ICAPS), October 2020. [Supplementary Material] |
4. R. Ajoodha, B. Rosman. Learning the Influence between Partially Observable Processes using Score- based Structure Learning. Advances in Science, Technology and Engineering Systems Journal, Volume 5(5) 2020. |
5. R. Ajoodha, B. Rosman. Discovery of Influence between Processes Represented by Hidden Markov Models. International IOT, Electronics and Mechatronics Conference (IEMTRONICS), September 2020. |
6. S. James, B. Rosman, G. Konidaris. Learning Portable Representations for High-Level Planning. International Conference on Machine Learning, July 2020. |
7. S. James, B. Rosman, G. Konidaris. Learning Object-Centric Representations for High-Level Planning in Minecraft. Object-Oriented Learning (OOL): Perception, Representation, and Reasoning. Workshop at ICML, July 2020. |
8. G. Nangue Tasse, S. James, B. Rosman. Logical Composition for Lifelong Reinforcement Learning. 4th Lifelong Learning Workshop at ICML, July 2020. |
9. A. Pretorius, E. van Biljon, B. van Niekerk, R. Eloff, M. Reynard, S. James, B. Rosman, H. Kamper, S. Kroon. If dropout limits trainable depth, does critical initialisation still matter? A large-scale statistical analysis on ReLU networks. Pattern Recognition Letters, Elsevier, June 2020. |
10. M. Carman, B. Rosman. Applying a principle of explicability to AI research in Africa: should we do it? Ethics and Information Technology, 2020. |
11. G. Nangue Tasse, S. James, B. Rosman. A Boolean Task Algebra for Reinforcement Learning. Beyond “Tabula Rasa” in Reinforcement Learning (BeTR-RL): Agents that remember, adapt, and generalize (Workshop at ICLR), April 2020. |
12. M. Cockcroft, S. Mawjee, S. James, P. Ranchod. Learning Options from Demonstration using Skill Segmentation. SAUPEC/ROBMECH/PRASA International Conference, January 2020. |
13. N. Kooverjee, S. James, T. van Zyl. Inter-and Intra-domain Knowledge Transfer for Related Tasks in Deep Character Recognition. SAUPEC/ROBMECH/PRASA International Conference, January 2020. |
14. K. Paupamah, S. James, R. Klein. Quantisation and Pruning for Neural Network Compression and Regularisation. SAUPEC/ROBMECH/PRASA International Conference, January 2020. |
15. M. Reynard, H. Engelbrecht, H. Kamper, B. Rosman. Combining primitive DQNs for improved reinforcement learning in Minecraft. SAUPEC/ROBMECH/PRASA International Conference, January 2020. |
16. P. Burke, R. Klein. Confident in the Crowd: Bayesian Inference to Improve Data Labelling in Crowdsourcing. SAUPEC/ROBMECH/PRASA International Conference, January 2020. |
17. J. Oyasor, M. Raborife and P. Ranchod. Sentiment Analysis as an Indicator to Evaluate Gender disparity on Sexual Violence Tweets in South Africa. SAUPEC/ROBMECH/PRASA International Conference, January 2020. |
2019
1. E. Boje, R.L. Christopher, J. Fernandes, J.H. Hepworth, R.B. Kuriakose, K. Kruger, T. Lorimer, N. Luwes, H.D. Mouton, A. Patel, B. Rosman, W.J. Smit, R. Stopforth, B. van Eden, T. van Niekerk, H. Vermaak, D. Withey. A Review of Robotics Research in South Africa. R&D Journal, 35:75-97, 2019. |
2. P. Moodley, B. Rosman, X. Hong. Understanding Structure of Concurrent Actions. AI-2019: The Thirty-ninth SGAI International Conference, December 2019. |
3. B. van Niekerk*, S. James*, A. Earle, B. Rosman. Composing Value Functions in Reinforcement Learning. International Conference on Machine Learning, June 2019. [Supplementary Material] |
4. O.C. Görür, B. Rosman, S. Albayrak. Anticipatory Bayesian Policy Selection for Online Adaptation of Collaborative Robots to Unknown Human Types. International Conference on Autonomous Agents and Multiagent Systems, May 2019. |
5. C. Bester, S. James, G. Konidaris. Multi-Pass Q-Networks for Deep Reinforcement Learning with Parameterised Action Spaces. Technical Report, May 2019. |
6. D. Bhugwan, P. Ranchod, R. Klein, B. Rosman. A comparison between fully connected and deconvolutional layers for road segmentation from satellite imagery. SAUPEC/ROBMECH/PRASA International Conference, January 2019. |
7. N. Makondo, B. Rosman. Towards improving incremental learning of manipulator kinematics with inter-robot knowledge transfer. SAUPEC/ROBMECH/PRASA International Conference, January 2019. |
8. B. van Eden, B. Rosman. An overview of robot vision. SAUPEC/ROBMECH/PRASA International Conference, January 2019. |
2018
1. O. Marom, B. Rosman. Zero-Shot Transfer with Deictic Object-Oriented Representation in Reinforcement Learning. Advances in Neural Information Processing Systems (NeurIPS), December 2018. [Supplementary Material] |
2. A. Bashir, A. Hassan, B. Rosman, D. Duma, M Ahmed. Implementation of A Neural Natural Language Understanding Component for Arabic Dialogue Systems. The 4th International Conference on Arabic Computational Linguistics (ACLing), November 2018. |
3. R. Fisher, B. Rosman, V. Ivan. Real-time Motion Planning in Changing Environments Using Topology-based Encoding of Past Knowledge. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2018. |
4. B. van Niekerk, S. James, A. Earle, B. Rosman. Will it Blend? Composing Value Functions in Reinforcement Learning. The 2nd Lifelong Learning: A Reinforcement Learning Approach (LLARLA) Workshop @ FAIM, July 2018. |
5. S. James, B. Rosman, G. Konidaris. Learning to Plan with Portable Symbols. ICML/IJCAI/AAMAS 2018 Workshop on Planning and Learning, July 2018. |
6. N. Makondo, B. Rosman, O. Hasegawa. Accelerating model learning with inter-robot knowledge transfer. IEEE International Conference on Robotics and Automation, May 2018. |
7. A. Earle, A. Saxe, B. Rosman. Hierarchical Subtask Discovery with Non-Negative Matrix Factorization. Proceedings of the Sixth International Conference on Learning Representations, April 2018. |
8. N. Makondo, M. Hiratsuka, B. Rosman, O. Hasegawa. A Non-linear Manifold Alignment Approach to Robot Learning from Demonstrations. Journal of Robotics and Mechatronics 30(2), April 2018. |
9. O.C. Görür, B. Rosman, F. Sivrikaya, S. Albayrak. Social Cobots: Anticipatory Decision-Making for Collaborative Robots Incorporating Unexpected Human Behaviors. ACM/IEEE International Conference on Human-Robot Interaction, March 2018. |
10. R. Ajoodha, B. Rosman. Learning the Influence Structure between Partially Observed Stochastic Processes using IoT Sensor Data. SmartIoT: AI Enhanced IoT Data Processing for Intelligent Applications at AAAI-18, February 2018. |
11. O. Marom, B. Rosman. Bayesian Reward Shaping in Reinforcement Learning. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, February 2018. [Supplementary Material] |
12. T. Taniguchi, E. Ugur, M. Hoffmann, L. Jamone, T. Nagai, B. Rosman, T. Matsuka, N. Iwahashi, E. Oztop, J. Piater, F. Worgotter. Symbol Emergence in Cognitive Developmental Systems: a Survey. IEEE transactions on Cognitive and Developmental Systems, January 2018. |
13. C. Innes, A. Lascarides, S.V. Albrecht, S. Ramamoorthy, B. Rosman. Reasoning about Unforeseen Possibilities During Policy Learning. Technical Report, January 2018. |
2017
1. R. Ajoodha, B. Rosman. Tracking Influence between Naïve Bayes Models using Score-Based Structure Learning. PRASA-RobMech International Conference, November 2017. |
2. J. Perlow, B. Rosman, B. Hayes, P. Ranchod. Raw Material Selection for Object Construction. PRASA-RobMech International Conference, November 2017. |
3. L. Darlow, B. Rosman. Fingerprint Minutiae Extraction using Deep Learning. International Joint Conference on Biometrics, October 2017. |
4. B. van Niekerk, A. Damianou, B. Rosman. Online Constrained Model-based Reinforcement Learning. Uncertainty in Artificial Intelligence, August 2017. |
5. A. Earle, A. Saxe, B. Rosman. Hierarchical Subtask Discovery With Non-Negative Matrix Factorization. Workshop on Lifelong Learning: A Reinforcement Learning Approach at ICML, August 2017. |
6. A. Saxe, A. Earle, B. Rosman. Hierarchy Through Composition with Multitask LMDPs. International Conference on Machine Learning, August 2017. |
7. A. Saxe, A. Earle, B. Rosman. Hierarchy Through Composition with Multitask LMDPs. International Conference on Machine Learning, August 2017. [Supplementary Material] |
8. O.C. Görür, B. Rosman, G. Hoffman, S. Albayrak. Toward Integrating Theory of Mind into Adaptive Decision-Making of Social Robots to Understand Human Intention. Workshop on the Role of Intentions in Human-Robot Interaction at the International Conference on Human-Robot Interaction, March 2017. |
9. S. James, G.D. Konidaris, B. Rosman. An Analysis of Monte Carlo Tree Search. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 2017. |
2016
1. A. Saxe, A. Earle, B. Rosman. Hierarchy through Composition with Linearly Solvable Markov Decision Processes. Technical report, December 2016. |
2. R. Berman, R. Benade, B. Rosman, P. Nordengen. Hyperformance: Predicting High-speed Performance of a B-Double. Fourteenth International Symposium on Heavy Vehicle Transport Technology, November 2016. |
3. M. Hiratsuka, N. Makondo, B. Rosman, O. Hasegawa. Trajectory Learning from Human Demonstrations via Manifold Mapping. IEEE/RSJ International Conference on Intelligent Robots and Systems, October 2016. |
4. S. James, B. Rosman, G. Konidaris. An Investigation into the Effectiveness of Heavy Rollouts in UCT. General Intelligence in Game-Playing Agents (GIGA'16) Workshop at IJCAI, July 2016. |
5. B. Rosman, M. Hawasly, S. Ramamoorthy. Bayesian Policy Reuse. Machine Learning Journal, 104(1), pp. 99-127, June 2016. |
6. P. Hernandez-Leal, M. Taylor, B. Rosman, E. L. Sucar, E. Munoz de Cote. A Bayesian approach for Learning and Tracking Switching, Non-stationary Opponents. Autonomous Agents and Multiagent Systems, May 2016. [Extended Abstract] |
7. W. Masson, P. Ranchod, and G. Konidaris. Reinforcement Learning with Parameterized Actions. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 2016. |
8. P. Hernandez-Leal, M. Taylor, B. Rosman, E. L. Sucar, E. Munoz de Cote. Identifying and Tracking Switching, Non-stationary Opponents: a Bayesian Approach. Workshop on Multiagent Interaction without Prior Coordination (MIPC), at AAAI, February 2016. |
2015
1. R. Ajoodha, R. Klein, B. Rosman. Single-labelled Music Genre Classification Using Content-Based Features. PRASA-RobMech International Conference, November 2015. |
2. R. Berman, R. Benade, B. Rosman. Autonomous Prediction of Performance-based Standards for Heavy Vehicles. PRASA-RobMech International Conference, November 2015. |
3. N. Makondo, B. Rosman, O. Hasegawa. Knowledge Transfer for Learning Robot Models via Local Procrustes Analysis. IEEE-RAS International Conference on Humanoid Robots, November 2015. |
4. P. Ranchod, B. Rosman, G. Konidaris. Nonparametric Bayesian Reward Segmentation for Skill Discovery Using Inverse Reinforcement Learning. IEEE/RSJ International Conference on Intelligent Robots and Systems, September 2015. |
5. B. Rosman, B. Hayes, B. Scassellati. Enhancing Agent Safety through Autonomous Environment Adaptation. Proceedings of the 5th joint IEEE International Conference on Development and Learning and on Epigenetic Robotics. Providence, Rhode Island, August 2015. |
6. A. Kleinhans, B. Rosman, M. Michalik, B. Tripp, R. Detry. G3DB: A Database of Successful and Failed Grasps with RGB-D Images, Point Clouds, Mesh Models and Gripper Parameters. Workshop on Robotic Hands, Grasping, and Manipulation, at the IEEE International Conference on Robotics and Automation, May 2015. [Extended Abstract] |
7. B. Rosman, S. Ramamoorthy. Action Priors for Learning Domain Invariances. IEEE Transactions on Autonomous Mental Development, January 2015. |
2014
1. B. Rosman. Context-based Online Policy Instantiation for Multiple Tasks and Changing Environments. RobMech/PRASA/AfLaT, November 2014. |
2. B. van Eden, B. Rosman, D. Withey, T. Ratshidaho, M. Keaikitse, D. Masha, A. Kleinhans, A. Shaik. CHAMP: a Bespoke Integrated System for Mobile Manipulation. RobMech/PRASA/AfLaT, November 2014. |
3. B. Rosman. Behavioural Domain Knowledge Transfer for Autonomous Agents. AAAI Fall Symposium on Knowledge, Skill, and Behavior Transfer in Autonomous Robots, November 2014. |
4. B. Rosman. Feature Selection for Domain Knowledge Representation through Multitask Learning. IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EpiRob), October 2014. |
5. A. Kleinhans, R. Detry, S. Thill, B. Rosman, B. Tripp. Modelling primate control of grasping for robotics applications. Second Workshop on Affordances: Visual Perception of Affordances and Functional Visual Primitives for Scene Analysis (with ECCV), September 2014. |
6. B. Rosman. Learning Domain Abstractions for Long Lived Robots. PhD Thesis. The University of Edinburgh, August 2014. |
7. M.M.H. Mahmud, B. Rosman, S. Ramamoorthy, P. Kohli. Adapting Interaction Environments to Diverse Users through Online Action Set Selection. In Proc. AAAI Workshop on Machine Learning for Interactive Systems (AAAI-MLIS), July 2014. |
8. B. Rosman, S. Ramamoorthy. Giving Advice to Agents with Hidden Goals. In Proc. International Conference on Robotics and Automation (ICRA), May 2014. |
9. B. Rosman, S. Ramamoorthy, M.M.H. Mahmud, P. Kohli. On User Behaviour Adaptation Under Interface Change. In Proc. International Conference on Intelligent User Interfaces (IUI), February 2014. |
10. M.M.H. Mahmud, M. Hawasly, B. Rosman, S. Ramamoorthy. Clustering Markov Decision Processes for Continual Transfer. Technical Report, University of Edinburgh, January 2014. |
2013 AND EARLIER
1. S. Ramamoorthy, M.M.H. Mahmud, B. Rosman, P. Kohli. Latent-variable MDP models for adapting the interaction environment of diverse users. Technical Report, University of Edinburgh, January 2013. |
2. B. Rosman, S. Ramamoorthy. What Good are Actions? Accelerating Learning using Learned Action Priors. IEEE International Conference on Development and Learning (ICDL-EpiRob), November 2012. [Paper of Excellence Award] |
3. B. Rosman, S. Ramamoorthy. A Multitask Representation using Reusable Local Policy Templates. AAAI 2012 Spring Symposium Series on Designing Intelligent Robots: Reintegrating AI, March 2012. |
4. B. Rosman, S. Ramamoorthy. Learning Spatial Relationships between Objects. International Journal of Robotics Research, Special Issue on Semantic Perception for Robots in Indoor Environments, vol. 30, 11: pp. 1328-13, September 2011. |
5. B. Rosman, S. Ramamoorthy. A Game Theoretic Procedure for Learning Hierarchically Structured Strategies. IEEE International Conference on Robotics and Automation, May 2010. |
9. S. Rauchas, B. Rosman, G.D. Konidaris and I.D. Sanders. Language Performance at High School and Success in First Year Computer Science. SIGCSE Technical Symposium on Computer Science Education, March 2006. |