All Outputs (8)

Quality-Diversity Optimisation on a Physical Robot Through Dynamics-Aware and Reset-Free Learning (2023)
Conference Proceeding
Smith, S. C., Lim, B., Janmohamed, H., & Cully, A. (2023). Quality-Diversity Optimisation on a Physical Robot Through Dynamics-Aware and Reset-Free Learning. In GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation (171-174). https://doi.org/10.1145/3583133.3590625

Learning algorithms, like Quality-Diversity (QD), can be used to acquire repertoires of diverse robotics skills. This learning is commonly done via computer simulation due to the large number of evaluations required. However, training in a virtual en... Read More about Quality-Diversity Optimisation on a Physical Robot Through Dynamics-Aware and Reset-Free Learning.

Hierarchical quality-diversity for online damage recovery (2022)
Conference Proceeding
Allard, M., Smith, S. C., Chatzilygeroudis, K., & Cully, A. (2022). Hierarchical quality-diversity for online damage recovery. In J. E. Fieldsend (Ed.), GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference (58-67). https://doi.org/10.1145/3512290.3528751

Adaptation capabilities, like damage recovery, are crucial for the deployment of robots in complex environments. Several works have demonstrated that using repertoires of pre-trained skills can enable robots to adapt to unforeseen mechanical damages... Read More about Hierarchical quality-diversity for online damage recovery.

Self-Explainable Robots in Remote Environments (2021)
Conference Proceeding
Chiyah Garcia, F. J., Smith, S. C., Lopes, J., Ramamoorthy, S., & Hastie, H. (2021). Self-Explainable Robots in Remote Environments. In C. Bethel, & A. Paiva (Eds.), HRI '21 Companion: Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction (662-664). https://doi.org/10.1145/3434074.3447275

As robots and autonomous systems become more adept at handling complex scenarios, their underlying mechanisms also become increasingly complex and opaque. This lack of transparency can give rise to unverifiable behaviours, limiting the use of robots... Read More about Self-Explainable Robots in Remote Environments.

Counterfactual Explanation and Causal Inference In Service of Robustness in Robot Control (2020)
Conference Proceeding
Smith, S. C., & Ramamoorthy, S. (2020). Counterfactual Explanation and Causal Inference In Service of Robustness in Robot Control. In 2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). https://doi.org/10.1109/icdl-epirob48136.2020.9278061

We propose an architecture for training generative models of counterfactual conditionals of the form, `can we modify event A to cause B instead of C?', motivated by applications in robot control. Using an `adversarial training' paradigm, an image-bas... Read More about Counterfactual Explanation and Causal Inference In Service of Robustness in Robot Control.

Semi-supervised learning from demonstration through program synthesis: An inspection robot case study (2020)
Conference Proceeding
Smith, S. C., & Ramamoorthy, S. (2020). Semi-supervised learning from demonstration through program synthesis: An inspection robot case study. In R. C. Cardoso, A. Ferrando, D. Briola, C. Menghi, & T. Ahlbrecht (Eds.), Proceedings of the First Workshop on Agents and Robots for reliable Engineered Autonomy (81-101)

Semi-supervised learning improves the performance of supervised machine learning by leveraging methods from unsupervised learning to extract information not explicitly available in the labels. Through the design of a system that enables a robot to le... Read More about Semi-supervised learning from demonstration through program synthesis: An inspection robot case study.

Decoupled Sampling-Based Motion Planning for Multiple Autonomous Marine Vehicles (2018)
Conference Proceeding
Volpi, N. C., Smith, S. C., Pascoal, A. M., Simetti, E., Turetta, A., Alibani, M., & Polani, D. (2018). Decoupled Sampling-Based Motion Planning for Multiple Autonomous Marine Vehicles. In OCEANS 2018 MTS/IEEE Charleston. https://doi.org/10.1109/oceans.2018.8604908

There is increasing interest in the deployment and operation of multiple autonomous marine vehicles (AMVs) for a number of challenging scientific and commercial operational mission scenarios. Some of the missions, such as geotechnical surveying and 3... Read More about Decoupled Sampling-Based Motion Planning for Multiple Autonomous Marine Vehicles.

Homeokinetic Reinforcement Learning (2012)
Conference Proceeding
Smith, S. C., & Herrmann, J. M. (2012). Homeokinetic Reinforcement Learning. In F. Schwenker, & E. Trentin (Eds.), Partially Supervised Learning. PSL 2011 (82-91). https://doi.org/10.1007/978-3-642-28258-4_9

In order to find a control policy for an autonomous robot by reinforcement learning, the utility of a behaviour can be revealed locally through a modulation of the motor command by probing actions. For robots with many degrees of freedom, this type o... Read More about Homeokinetic Reinforcement Learning.

Clustering-Based Searching and Navigation in an Online News Source (2006)
Conference Proceeding
Smith, S. C., & Rodríguez, M. A. (2006). Clustering-Based Searching and Navigation in an Online News Source. In M. Lalmas, A. MacFarlane, S. Rüger, A. Tombros, T. Tsikrika, & A. Yavlinsky (Eds.), Advances in Information Retrieval. ECIR 2006 (143-154). https://doi.org/10.1007/11735106_14

The growing amount of online news posted on the WWW demands new algorithms that support topic detection, search, and navigation of news documents. This work presents an algorithm for topic detection that considers the temporal evolution of news and t... Read More about Clustering-Based Searching and Navigation in an Online News Source.