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Found 16 papers, 2 papers with code
Fin-QD: A Computational Design Framework for Soft Grippers: Integrating MAP-Elites and High-fidelity FEM
This work first discusses a significantly large design space (28 design parameters) for a finger-based soft gripper, including the rarely-explored design space of finger arrangement that is converted to various configurations to arrange individual soft fingers.
Prospectively accelerated dynamic speech MRI at 3 Tesla using a self-navigated spiral based manifold regularized scheme
Our scheme provided improved reconstruction over the others.
Assessing Evolutionary Terrain Generation Methods for Curriculum Reinforcement Learning
To allow direct comparison between both direct and indirect representations, we assess the impact of a range of representation-agnostic MAP-Elites feature descriptors that compute metrics directly from the generated terrain meshes.
Semi-supervised Gated Recurrent Neural Networks for Robotic Terrain Classification
Legged robots are popular candidates for missions in challenging terrains due to the wide variety of locomotion strategies they can employ.
Diversity-based Design Assist for Large Legged Robots
We combine MAP-Elites and highly parallelisable simulation to explore the design space of a class of large legged robots, which stand at around 2m tall and whose design and construction is not well-studied.
Environmental Adaptation of Robot Morphology and Control through Real-world Evolution
We find that the evolutionary search finds high-performing and diverse morphology-controller configurations by adapting both control and body to the different properties of the physical environments.
Towards Crossing the Reality Gap with Evolved Plastic Neurocontrollers
A critical issue in evolutionary robotics is the transfer of controllers learned in simulation to reality.
Parameter Optimization and Learning in a Spiking Neural Network for UAV Obstacle Avoidance targeting Neuromorphic Processors
The Lobula Giant Movement Detector (LGMD) is an identified neuron of the locust that detects looming objects and triggers the insect's escape responses.
Evolving Spiking Neural Networks for Nonlinear Control Problems
Spiking Neural Networks are powerful computational modelling tools that have attracted much interest because of the bioinspired modelling of synaptic interactions between neurons.
Comparing Direct and Indirect Representations for Environment-Specific Robot Component Design
The second, an indirect method, utilises CPPN-NEAT.
Evolving embodied intelligence from materials to machines
Natural lifeforms specialise to their environmental niches across many levels; from low-level features such as DNA and proteins, through to higher-level artefacts including eyes, limbs, and overarching body plans.
Quantifying the Reality Gap in Robotic Manipulation Tasks
We quantify the accuracy of various simulators compared to a real world robotic reaching and interaction task.
Robotics
Towards the Targeted Environment-Specific Evolution of Robot Components
This research considers the task of evolving the physical structure of a robot to enhance its performance in various environments, which is a significant problem in the field of Evolutionary Robotics.
Differential Evolution and Bayesian Optimisation for Hyper-Parameter Selection in Mixed-Signal Neuromorphic Circuits Applied to UAV Obstacle Avoidance
We also investigate the use of Self-Adaptive Differential Evolution (SADE) which has been shown to ameliorate the difficulties of finding appropriate input parameters for DE.
Evolving Unipolar Memristor Spiking Neural Networks
Neuromorphic computing --- brainlike computing in hardware --- typically requires myriad CMOS spiking neurons interconnected by a dense mesh of nanoscale plastic synapses.
A Cognitive Architecture Based on a Learning Classifier System with Spiking Classifiers
Learning Classifier Systems (LCS) are population-based reinforcement learners that were originally designed to model various cognitive phenomena.
