Evolving virtual creatures is a field with a rich history and recently it has been getting more attention, especially in the soft robotics domain. The compliance of soft materials endows soft robots with complex behavior, but it also makes their design process unintuitive and in need of automated design. Despite the great interest, evolved virtual soft robots lack the complexity, and co-optimization of morphology and control remains a challenging problem. Prior work identifies and investigates a major issue with the co-optimization process -- fragile co-adaptation of brain and body resulting in premature convergence of morphology. In this work, we expand the investigation of this phenomenon by comparing learnable controllers with proprioceptive observations and fixed controllers without any observations, whereas in the latter case, we only have the optimization of the morphology. Our experiments in two morphology spaces and two environments that vary in complexity show, concrete examples of the existence of high-performing regions in the morphology space that are not able to be discovered during the co-optimization of the morphology and control, yet exist and are easily findable when optimizing morphologies alone. Thus this work clearly demonstrates and characterizes the challenges of optimizing morphology during co-optimization. Based on these results, we propose a new body-centric framework to think about the co-optimization problem which helps us understand the issue from a search perspective. We hope the insights we share with this work attract more attention to the problem and help us to enable efficient brain-body co-optimization.

The paper focuses on how a model could be learnt for determining at runtime how much of spoken input needs to be understood, and what configuration of processes can be expected to yield that result. Typically, a dialogue system applies a fixed configuration of shallow and deep forms of processing to its input. The configuration tries to balance robustness with depth of understanding, creating a system that always tries to understand as well as it can. The paper adopts a different view, assuming that what needs to be understood can vary per context. To facilitate this any-depth processing, the paper proposes an approach based on learnable controllers. The paper illustrates the main ideas of the approach on examples from a robot acquiring situated dialogue competence, and a robot working with users on a task.