Carina Veil develops intelligent control algorithms for soft robots that move like trunks, vines, or tentacles
By combining control theory, biomechanics, and artificial intelligence, she addresses the complex dynamics of soft robotic structures and designs motion that is as safe and precise as the bodies they mimic.
What is your role and what does it involve?
I’m a postdoctoral researcher at Stanford University in the Department of Mechanical Engineering, more precisely the Living Matter Lab, where my research focuses on bioinspired robots and how to control them. I like to see it as a discipline where control engineering and artificial intelligence meet the principles of nature and medicine. What can we learn from muscles and their actuation principles in elephant trunks or octopus arms to design and control soft robotic structures with high precision and inherent safety? Since I started recently, I’m currently building a network within the Robotics, AI, and Bioengineering Departments, and find out what other people are excited about. It’s important to me to stay rooted in both the robotics/control and the biomedical science communities. That’s why I’m actively involved in multiple IEEE societies: I serve as a Chair of the IEEE Engineering Medicine and Biology (EMBS) Germany Chapter and as a Communications Co-Chair of NextCom, a committee by the IEEE Control System Society (CSS) to support and connect early-career researchers in control. In my opinion, your horizon as a researcher can never be too broad! 😊
What do you find most interesting or enjoyable about your work?
Stanford is an incredibly vibrant environment for young researchers, offering amazing opportunities to grow. There are countless chances to learn about other fields — from attending lunch seminars (and yes, grabbing free lunch 😊), to joining mentoring workshops, or connecting with peers during postdoc happy hours. Unlike many places, you can really feel that most people are here because they want to be — they’re passionate, driven, and curious. That atmosphere naturally creates a bit of competition, but it’s also super inspiring and motivating.
As a postdoc, I really value the freedom to explore areas in soft robotics that I’m interested in. I have the time and space to dive into new methods, think creatively about potential future directions, and I am actively encouraged to try out unconventional things.
Tell us about your research (include an explicitly stated long-term goal)
My research is about learning-enhanced control for bioinspired robots. Why bioinspired? Take elephants, for example — their trunks are continuously deformable, yet they use them with remarkable dexterity to grasp objects in narrow or distant locations. The mechanics of these biological structures differ significantly from traditional rigid robots. Muscles, tendons, and ligaments allow robust and safe interaction with the environment — something we aim to translate into robotics.
Currently, my research focuses on transferring insights from biomechanical model structures to robotics by investigating trunk-inspired robots made from active filaments. An active filament is a slender, deformable structure that can generate internal stress and shape change in response to localized activation, such as contraction, elongation, or other microscale remodeling of embedded fibers. Unlike passive filaments (like ropes or beams), active filaments can change their intrinsic geometry (curvature, torsion, and extension) due to these internal actuation fields. This activation mimics biological mechanisms seen in systems like plant stems, octopus arms, or elephant trunks, where fiber-like structures contract or grow to produce smooth, spatially distributed motion. By modeling soft robotic structures with active filaments, we can build systems that are both mechanically accurate and biologically inspired, which is ideal playground for simulating and controlling soft robots.
My long-term research goal is to integrate as much physics and biology in soft robotics as possible while still offering enough approximations for real worlds task. I would love to see soft robots come to life for medical applications in endoscopy or rehabilitation. As a control engineer, I want to create a library of methods to control bioinspired robots, mainly through physics-informed learning - along with stability investigations, safety considerations, experimental validations, and generalizations to other soft robot dynamics.
What working achievement and/or initiative are you most proud of?
My biggest achievement is simply standing where I am today: a postdoc at Stanford, one of the leading universities in the world, working in a research area that I am super excited about! Coming from a working class background and raised by a single mum, I didn’t have much academic background to lean on. When I was a teenager and shared my interest in traveling, languages, and math, the expectation was that I’d pursue an apprenticeship at a local bank or travel agency. Choosing to study mechanical engineering — and doing so without much support or guidance — was a path full of challenges. At times, I wished for more direction. But going against the odds and getting here makes me incredibly proud. At this point, a huge shoutout to all my mentors that pushed me to go further and to believe in myself. That support was invaluable. I hope I can be this person for someone else in the future.
What’s next on the research horizon for you?
I’ve just started my position at Stanford this year, and over the next 2–3 years, I’m excited to really dig deep into soft robot control and explore new types of bioinspired robots. I’m particularly interested in how we can apply physics-informed machine learning and operator learning to improve the modeling, control, or motion planning aspect in soft robotics, while not losing the intuitive and systematic approach to problems that system dynamics and control theory offer. My current focus is on trunk-inspired robots, but I’m also very curious about other architectures, such as tensegrity robots and vine robots, which open the field for many new applications in sustainability and medicine and offer fascinating challenges for control theory and design.