This research investigates biomimicry-inspired kinematics in soft robotics through neural field based rendering pipelines, leveraging the existing differentiable simulation framework for soft robot kinematics developed in the Digital Manufacturing Lab at the University of Manchester. The project takes inspiration from the intricate motion and deformation patterns observed in nature, aiming to replicate these adaptive behaviours in soft robots to achieve enhanced versatility and functionality. The foundation of this work lies in the differentiable simulation pipeline, which enables precise modelling and optimization of soft robotic systems. Building on this framework, the project will incorporate neural field based rendering techniques to establish a self-modelling mechanism, allowing robots to autonomously learn and adapt their kinematics based on bio-inspired principles. Motion and deformation data from animals will be collected and analysed to serve as a reference for designing and optimizing robotic movements.

Through this approach, the research seeks to bridge the gap between biological motion and robotic implementation. By integrating neural representations with optimization methods, the project will develop a system capable of dynamically aligning soft robotic deformations with natural motion patterns. This self-modelling capability not only enhances adaptability but also ensures efficient performance in complex, unstructured environments. Potential applications include medical robotics, environmental exploration, and tasks requiring dynamic interaction, where soft robots must adapt to changing conditions. This research will contribute to advancing the field of biomimetic robotics by combining nature-inspired design principles with state-of-the-art computational techniques, setting a new standard for the design and control of soft robotic systems.

The outcomes of this project are expected to provide valuable insights into the convergence of biomimicry, neural optimization, and robotics, paving the way for more intelligent, versatile, and efficient robotic solutions.

Before you apply: We strongly recommend that you contact the supervisor(s) for this project before you apply.

How to apply: To be considered for this project you’ll need to complete a formal application through our online application portal. This link should directly open an application for FSE Bicentenary PhD . Please select University of Manchester funding in the funding section of the form.

When applying, you’ll need to specify the full name of this project , the name of your proposed supervisor/s , details of your previous study, and names and contact details of two referees . You are also required to upload your CV and a Personal Statement describing your motivation for applying for the project.

Your application cannot be processed without all of the required documents, and we cannot accept responsibility for late or missed deadlines where applications are incomplete.

Equality, diversity and inclusion: Equality, diversity and inclusion are fundamental to the success of The University of Manchester, and are at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact. We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).

Eligibility

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or equivalent) in a relevant science or engineering related discipline.

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