All Outputs (6)

Understanding fitness landscapes in morpho-evolution via local optima networks (2024)
Conference Proceeding
Thomson, S. L., Le Goff, L., Hart, E., & Buchanan, E. (in press). Understanding fitness landscapes in morpho-evolution via local optima networks. . https://doi.org/10.1145/3638529.3654059

Morpho-Evolution (ME) refers to the simultaneous optimisation of a robot's design and controller to maximise performance given a task and environment. Many genetic encodings have been proposed which are capable of representing design and control. Pre... Read More about Understanding fitness landscapes in morpho-evolution via local optima networks.

On the challenges of jointly optimising robot morphology and control using a hierarchical optimisation scheme (2021)
Conference Proceeding
Goff, L. K. L., & Hart, E. (2021). On the challenges of jointly optimising robot morphology and control using a hierarchical optimisation scheme. In GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion (1498-1502). https://doi.org/10.1145/3449726.3463156

We investigate a hierarchical scheme for the joint optimisation of robot bodies and controllers in a complex morphological space. An evolutionary algorithm optimises body-plans while a separate learning algorithm is applied to each body generated to... Read More about On the challenges of jointly optimising robot morphology and control using a hierarchical optimisation scheme.

Evolution of Diverse, Manufacturable Robot Body Plans (2020)
Conference Proceeding
Buchanan, E., Le Goff, L., Hart, E., Eiben, A. E., De Carlo, M., Li, W., …Tyrrell, A. M. (2020). Evolution of Diverse, Manufacturable Robot Body Plans. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI) (2132-2139). https://doi.org/10.1109/SSCI47803.2020.9308434

Advances in rapid prototyping have opened up new avenues of research within Evolutionary Robotics in which not only controllers but also the body plans (morphologies) of robots can evolve in real-time and real-space. However, this also introduces new... Read More about Evolution of Diverse, Manufacturable Robot Body Plans.

Hardware Design for Autonomous Robot Evolution (2020)
Conference Proceeding
Hale, M. F., Angus, M., Buchanan, E., Li, W., Woolley, R., Le Goff, L. K., …Tyrrell, A. M. (2020). Hardware Design for Autonomous Robot Evolution. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI) (2140-2147). https://doi.org/10.1109/SSCI47803.2020.9308204

The long term goal of the Autonomous Robot Evolution (ARE) project is to create populations of physical robots, in which both the controllers and body plans are evolved. The transition for evolutionary designs from purely simulation environments into... Read More about Hardware Design for Autonomous Robot Evolution.

On Pros and Cons of Evolving Topologies with Novelty Search (2020)
Conference Proceeding
Le Goff, L. K., Hart, E., Coninx, A., & Doncieux, S. (2020). On Pros and Cons of Evolving Topologies with Novelty Search. In ALIFE 2020: The 2020 Conference on Artificial Life (423-431). https://doi.org/10.1162/isal_a_00291

Novelty search was proposed as a means of circumventing deception and providing selective pressure towards novel behaviours to provide a path towards open-ended evolution. Initial implementations relied on neuro-evolution approaches which increased n... Read More about On Pros and Cons of Evolving Topologies with Novelty Search.

Sample and time efficient policy learning with CMA-ES and Bayesian Optimisation (2020)
Conference Proceeding
Le Goff, L. K., Buchanan, E., Hart, E., Eiben, A. E., Li, W., De Carlo, M., …Tyrrell, A. M. (2020). Sample and time efficient policy learning with CMA-ES and Bayesian Optimisation. In ALIFE 2020: The 2020 Conference on Artificial Life (432-440). https://doi.org/10.1162/isal_a_00299

In evolutionary robot systems where morphologies and controllers of real robots are simultaneously evolved, it is clear that there is likely to be requirements to refine the inherited controller of a 'newborn' robot in order to better align it to its... Read More about Sample and time efficient policy learning with CMA-ES and Bayesian Optimisation.