b'David Howard'

research

∙ 08/03/2023

A Compliant Robotic Leg Based on Fibre Jamming

Humans possess a remarkable ability to react to sudden and unpredictable...

0 Lois Liow, et al. ∙

research

∙ 12/13/2022

A Comprehensive Dataset of Grains for Granular Jamming in Soft Robotics: Grip Strength and Shock Absorption

We test grip strength and shock absorption properties of various granula...

0 David Howard, et al. ∙

research

∙ 12/13/2022

Active Vibration Fluidization for Granular Jamming Grippers

Granular jamming has recently become popular in soft robotics with wides...

0 Cameron Coombe, et al. ∙

research

∙ 12/13/2022

The Jamming Donut: A Free-Space Gripper based on Granular Jamming

Fruit harvesting has recently experienced a shift towards soft grippers ...

0 Therese Joseph, et al. ∙

research

∙ 12/11/2022

Human-Robot Team Performance Compared to Full Robot Autonomy in 16 Real-World Search and Rescue Missions: Adaptation of the DARPA Subterranean Challenge

Human operators in human-robot teams are commonly perceived to be critic...

0 Nicole Robinson, et al. ∙

research

∙ 11/25/2022

Automated design of pneumatic soft grippers through design-dependent multi-material topology optimization

In recent years, soft robotic grasping has rapidly spread through the ac...

0 Josh Pinskier, et al. ∙

research

∙ 05/17/2022

EvoRobogami: Co-designing with Humans in Evolutionary Robotics Experiments

We study the effects of injecting human-generated designs into the initi...

0 Huang Zonghao, et al. ∙

research

∙ 03/29/2022

Assessing Evolutionary Terrain Generation Methods for Curriculum Reinforcement Learning

Curriculum learning allows complex tasks to be mastered via incremental ...

11 David Howard, et al. ∙

research

∙ 11/02/2021

Getting a Grip: in Materio Evolution of Membrane Morphology for Soft Robotic Jamming Grippers

The application of granular jamming in soft robotics is a recent and pro...

0 David Howard, et al. ∙

research

∙ 09/22/2021

Vibration Improves Performance in Granular Jamming Grippers

Granular jamming is a popular soft robotics technology that has seen rec...

0 Raghav Mishra, et al. ∙

research

∙ 09/10/2021

Jammkle: Fibre jamming 3D printed multi-material tendons and their application in a robotic ankle

Fibre jamming is a relatively new and understudied soft robotic mechanis...

0 James Brett, et al. ∙

research

∙ 09/10/2021

Follow the Gradient: Crossing the Reality Gap using Differentiable Physics (RealityGrad)

We propose a novel iterative approach for crossing the reality gap that ...

0 Jack Collins, et al. ∙

research

∙ 04/09/2021

Shape, Size, and Fabrication Effects in 3D Printed Granular Jamming Grippers

Granular jamming is a popular soft actuation mechanism that provides hig...

0 David Howard, et al. ∙

research

∙ 11/24/2020

Semi-supervised Gated Recurrent Neural Networks for Robotic Terrain Classification

Legged robots are popular candidates for missions in challenging terrain...

0 Ahmadreza Ahmadi, et al. ∙

research

∙ 06/05/2020

Path Towards Multilevel Evolution of Robots

Multi-level evolution is a bottom-up robotic design paradigm which decom...

0 Shelvin Chand, et al. ∙

research

∙ 05/19/2020

Real World Morphological Evolution is Feasible

Evolutionary algorithms offer great promise for the automatic design of ...

0 Tønnes F. Nygaard, et al. ∙

research

∙ 04/17/2020

Diversity-based Design Assist for Large Legged Robots

We combine MAP-Elites and highly parallelisable simulation to explore th...

0 David Howard, et al. ∙

research

∙ 03/30/2020

Environmental Adaptation of Robot Morphology and Control through Real-world Evolution

Robots operating in the real world will experience a range of different ...

0 Tønnes F. Nygaard, et al. ∙

research

∙ 03/03/2020

Traversing the Reality Gap via Simulator Tuning

The large demand for simulated data has made the reality gap a problem o...

0 Jack Collins, et al. ∙

research

∙ 02/23/2020

Crossing the Reality Gap with Evolved Plastic Neurocontrollers

A critical issue in evolutionary robotics is the transfer of controllers...

0 Huanneng Qiu, et al. ∙

research

∙ 11/05/2019

Benchmarking Simulated Robotic Manipulation through a Real World Dataset

We present a benchmark to facilitate simulated manipulation; an attempt ...

0 Jack Collins, et al. ∙

research

∙ 10/17/2019

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...

24 Llewyn Salt, et al. ∙

research

∙ 03/04/2019

Evolving Spiking Neural Networks for Nonlinear Control Problems

Spiking Neural Networks are powerful computational modelling tools that ...

0 Huanneng Qiu, et al. ∙

research

∙ 01/21/2019

Comparing Direct and Indirect Representations for Environment-Specific Robot Component Design

We compare two representations used to define the morphology of legs for...

2 Jack Collins, et al. ∙

research

∙ 01/17/2019

Evolving embodied intelligence from materials to machines

Natural lifeforms specialise to their environmental niches across many l...

10 David Howard, et al. ∙

research

∙ 11/05/2018

Quantifying the Reality Gap in Robotic Manipulation Tasks

We quantify the accuracy of various simulators compared to a real world ...

0 Jack Collins, et al. ∙

research

∙ 10/11/2018

Towards the Targeted Environment-Specific Evolution of Robot Components

This research considers the task of evolving the physical structure of a...

4 Jack Collins, et al. ∙

research

∙ 04/17/2017

Differential Evolution and Bayesian Optimisation for Hyper-Parameter Selection in Mixed-Signal Neuromorphic Circuits Applied to UAV Obstacle Avoidance

The Lobula Giant Movement Detector (LGMD) is a an identified neuron of t...

0 Llewyn Salt, et al. ∙

research

∙ 09/01/2015

Evolving Unipolar Memristor Spiking Neural Networks

Neuromorphic computing --- brainlike computing in hardware --- typically...

0 David Howard, et al. ∙

research

∙ 08/31/2015

A Cognitive Architecture Based on a Learning Classifier System with Spiking Classifiers

Learning Classifier Systems (LCS) are population-based reinforcement lea...

0 David Howard, et al. ∙

David Howard

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