All Outputs (4)

Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity (2023)
Journal Article
Allard, M., Smith, S. C., Chatzilygeroudis, K., Lim, B., & Cully, A. (2023). Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity. ACM Transactions on Evolutionary Learning and Optimization, 3(2), Article 6. https://doi.org/10.1145/3596912

In real-world environments, robots need to be resilient to damages and robust to unforeseen scenarios. Quality-Diversity (QD) algorithms have been successfully used to make robots adapt to damages in seconds by leveraging a diverse set of learned ski... Read More about Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity.

The DIAMOND Model: Deep Recurrent Neural Networks for Self-Organizing Robot Control (2020)
Journal Article
Smith, S. C., Dharmadi, R., Imrie, C., Si, B., & Herrmann, J. M. (2020). The DIAMOND Model: Deep Recurrent Neural Networks for Self-Organizing Robot Control. Frontiers in Neurorobotics, 14, Article 62. https://doi.org/10.3389/fnbot.2020.00062

The proposed architecture applies the principle of predictive coding and deep learning in a brain-inspired approach to robotic sensorimotor control. It is composed of many layers each of which is a recurrent network. The component networks can be spo... Read More about The DIAMOND Model: Deep Recurrent Neural Networks for Self-Organizing Robot Control.

Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop (2018)
Journal Article
Biehl, M., Guckelsberger, C., Salge, C., Smith, S. C., & Polani, D. (2018). Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop. Frontiers in Neurorobotics, 12, https://doi.org/10.3389/fnbot.2018.00045

Active inference is an ambitious theory that treats perception, inference, and action selection of autonomous agents under the heading of a single principle. It suggests biologically plausible explanations for many cognitive phenomena, including cons... Read More about Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop.

Evaluation of Internal Models in Autonomous Learning (2018)
Journal Article
Smith, S. C., & Herrmann, J. M. (2019). Evaluation of Internal Models in Autonomous Learning. IEEE Transactions on Cognitive and Developmental Systems, 11(4), 463-472. https://doi.org/10.1109/tcds.2018.2865999

Internal models (IMs) can represent relations between sensors and actuators in natural and artificial agents. In autonomous robots, the adaptation of IMs and the adaptation of the behavior are interdependent processes which have been studied under pa... Read More about Evaluation of Internal Models in Autonomous Learning.