Soft robots promise inherent safety via their material compliance for seamless interactions with humans or delicate environments. Yet, their development is challenging because it requires integrating materials, geometry, actuation, and autonomy into complex mechatronic systems. Despite progress, the field struggles to balance task-specific performance with broader factors like durability and manufacturability—a difficulty that we find is compounded by traditional sequential design processes with their lack of feedback loops. In this perspective, we review emerging co-design approaches that simultaneously optimize the body and brain, enabling the discovery of unconventional designs highly tailored to the given tasks. We then identify three key shortcomings that limit the broader adoption of such co-design methods within the soft robotics domain. First, many rely on simulation-based evaluations focusing on a single metric, while real-world designs must satisfy diverse criteria. Second, current methods emphasize computational modeling without ensuring feasible realization, risking sim-to-real performance gaps. Third, high computational demands limit the exploration of the complete design space. Finally, we propose a holistic co-design framework that addresses these challenges by incorporating a broader range of design values, integrating real-world prototyping to refine evaluations, and boosting efficiency through surrogate metrics and model-based control strategies. This holistic framework, by simultaneously optimizing functionality, durability, and manufacturability, has the potential to enhance reliability and foster broader acceptance of soft robotics, transforming human-robot interactions.

Artificial cilia are a prominent example of physical intelligence. Their mechanical properties are often designed so to achieve desired motions in response to very simple actuation patterns. Yet, variability in the mechanical properties are inherent in these systems. This may critically disrupt the input-output relation, resulting in a final behavior completely different from the desired one. In this Communication we investigate the possibility of designing a robotic brain that helps the cilium to maintain its physical intelligence. We achieve that by closing a model-based control loop which tracks the position of the end effector while compensating for drag forces. We propose experiments to characterize our model, and extensive simulations validating the results in different conditions. This work is intended as a proof of concept, which will be further expanded in future work.