no code implementations • 21 Nov 2023 • Yue Xie, Xing Wang, Fumiya Iida, David Howard
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.
1 code implementation • 6 Sep 2022 • Rushdi Zahid Rusho, Abdul Haseeb Ahmed, Stanley Kruger, Wahidul Alam, David Meyer, David Howard, Brad Story, Mathews Jacob, Sajan Goud Lingala
Our scheme provided improved reconstruction over the others.
no code implementations • 29 Mar 2022 • David Howard, Josh Kannemeyer, Davide Dolcetti, Humphrey Munn, Nicole Robinson
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.
1 code implementation • 24 Nov 2020 • Ahmadreza Ahmadi, Tønnes Nygaard, Navinda Kottege, David Howard, Nicolas Hudson
Legged robots are popular candidates for missions in challenging terrains due to the wide variety of locomotion strategies they can employ.
no code implementations • 17 Apr 2020 • David Howard, Thomas Lowe, Wade Geles
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.
no code implementations • 30 Mar 2020 • Tønnes F. Nygaard, Charles P. Martin, David Howard, Jim Torresen, Kyrre Glette
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.
no code implementations • 23 Feb 2020 • Huanneng Qiu, Matthew Garratt, David Howard, Sreenatha Anavatti
A critical issue in evolutionary robotics is the transfer of controllers learned in simulation to reality.
no code implementations • 17 Oct 2019 • Llewyn Salt, David Howard, Giacomo Indiveri, Yulia Sandamirskaya
The Lobula Giant Movement Detector (LGMD) is an identified neuron of the locust that detects looming objects and triggers the insect's escape responses.
no code implementations • 4 Mar 2019 • Huanneng Qiu, Matthew Garratt, David Howard, Sreenatha Anavatti
Spiking Neural Networks are powerful computational modelling tools that have attracted much interest because of the bioinspired modelling of synaptic interactions between neurons.
no code implementations • 21 Jan 2019 • Jack Collins, Ben Cottier, David Howard
The second, an indirect method, utilises CPPN-NEAT.
no code implementations • 17 Jan 2019 • David Howard, Agoston E. Eiben, Danielle Frances Kennedy, Jean-Baptiste Mouret, Philip Valencia, Dave Winkler
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.
no code implementations • 5 Nov 2018 • Jack Collins, David Howard, Jürgen Leitner
We quantify the accuracy of various simulators compared to a real world robotic reaching and interaction task.
Robotics
no code implementations • 11 Oct 2018 • Jack Collins, Wade Geles, David Howard, Frederic Maire
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.
no code implementations • 17 Apr 2017 • Llewyn Salt, David Howard, Giacomo Indiveri, Yulia Sandamirskaya
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.
no code implementations • 1 Sep 2015 • David Howard, Larry Bull, Ben De Lacy Costello
Neuromorphic computing --- brainlike computing in hardware --- typically requires myriad CMOS spiking neurons interconnected by a dense mesh of nanoscale plastic synapses.
no code implementations • 31 Aug 2015 • David Howard, Larry Bull, Pier-Luca Lanzi
Learning Classifier Systems (LCS) are population-based reinforcement learners that were originally designed to model various cognitive phenomena.