12 citations · 22 across the 8 of their papers we have counts for
8 papers
Generation of Diverse and Functional Robot Designs using Superquadrics Parametrisation and Quality-Diversity
Leni Le Goff, Simon Smith, Emma Hart
Generative design of robots requires navigating a vast search-space, encompassing physical configurations and behavioural parameters. Evolutionary Algorithms (EAs) have shown promi…
Quality-Diversity Optimisation on a Physical Robot Through Dynamics-Aware and Reset-Free Learning
Simón C. Smith, Bryan Lim, Hannah Janmohamed +1
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 l…
Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning
Manon Flageat, Bryan Lim, Luca Grillotti +3
We present a Quality-Diversity benchmark suite for Deep Neuroevolution in Reinforcement Learning domains for robot control. The suite includes the definition of tasks, environments…
Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity
Maxime Allard, Simón C. Smith, Konstantinos Chatzilygeroudis +2
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…
Hierarchical Quality-Diversity for Online Damage Recovery
Maxime Allard, Simón C. Smith, Konstantinos Chatzilygeroudis +1
Adaptation capabilities, like damage recovery, are crucial for the deployment of robots in complex environments. Several works have demonstrated that using repertoires of pre-train…
Attainment Regions in Feature-Parameter Space for High-Level Debugging in Autonomous Robots
Simón C. Smith, Subramanian Ramamoorthy
Understanding a controller's performance in different scenarios is crucial for robots that are going to be deployed in safety-critical tasks. If we do not have a model of the dynam…
Semi-supervised Learning From Demonstration Through Program Synthesis: An Inspection Robot Case Study
Simón C. Smith, Subramanian Ramamoorthy
Semi-supervised learning improves the performance of supervised machine learning by leveraging methods from unsupervised learning to extract information not explicitly available in…
Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop
Martin Biehl, Christian Guckelsberger, Christoph Salge +2
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 biologicall…