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It has been shown that manipulation of objects by 3D virtual creatures can play an important role in the evolution of complex, embodied sensorimotor behaviours. In this work we examine the capacity of virtual creatures that use evolutionary and control architectures already shown to be capable of sensor-differential gradient-following locomotion (tropotaxis) to adapt to solve a physical problem involving the manipulation of 3D objects in their environments. Specifically, the creatures task is to guide a physically-modelled cube through their environments in order to achieve maximum covered distance of the object. Agents were evolved in the manipulation environment from random initial genotypes and from genotypes previously optimised for performance in a different task. Performance was evaluated both before and after evolutionary adaptation. We show that the architecture achieves embodied feedback control in the block movement task. We observed some overlap between the earlier and later environments but also that success in the first environment does not preclude or entail success in the second. We found that species evolving from scratch do no better or worse than those optimised for a different environment, and that sensory feedback is necessary for correct approach and control behaviours in agents, although close control is less dependent on sensory input than distance approach.

We describe the content and outcomes of the First Workshop on Open-Ended Evolution: Recent Progress and Future Milestones (OEE1), held during the ECAL 2015 conference at the University of York, UK, in July 2015. We briefly summarize the content of the workshop's talks, and identify the main themes that emerged from the open discussions. Two important conclusions from the discussions are: (1) the idea of pluralism about OEE—it seems clear that there is more than one interesting and important kind of OEE; and (2) the importance of distinguishing observable behavioral hallmarks of systems undergoing OEE from hypothesized underlying mechanisms that explain why a system exhibits those hallmarks. We summarize the different hallmarks and mechanisms discussed during the workshop, and list the specific systems that were highlighted with respect to particular hallmarks and mechanisms. We conclude by identifying some of the most important open research questions about OEE that are apparent in light of the discussions. The York workshop provides a foundation for a follow-up OEE2 workshop taking place at the ALIFE XV conference in Cancún, Mexico, in July 2016. Additional materials from the York workshop, including talk abstracts, presentation slides, and videos of each talk, are available at http://alife.org/ws/oee1.

Following earlier work on the neuroevolution of deliberative behaviour to solve increasingly challenging tasks in a twodimensional dynamic world, this paper presents the results of extending the original system to a three-dimensional rigid body simulation. The 3D physically based setting requires that a successful agent continually and deliberately adjust its gait, turning and other motor control over the many stages and sub-stages of these tasks, within its individual evaluation. Achieving such complex interplay between motor control and deliberative control, within a neuroevolutionary framework, is the focus of this work. To this end, a novel neural architecture is presented and an incremental evolutionary approach used to bootstrap the locomotive behaviour of the agents. Agent morphology is fixed as a quadruped with three degrees of freedom per limb. Agent populations have no initial knowledge of the problem domain, and evolve to move around and then solve progressively more difficult challenges in the environment using a tournament-based co-evolutionary algorithm. The results demonstrate not only success at the tasks but also a variety of intricate lifelike behaviours being used, separately and in combination, to achieve this success. Given the problem-agnostic controller architecture, these results indicate a potential for discovering yet more advanced behaviours in yet more complex environments.