Soft and Biohybrid Robotics Course

Course Abstract

The course leverages nature-inspired strategies to create safer, more adaptive robotic systems. We explore how to design, model, and build soft robots using flexible or living materials, enabling greater adaptability in manipulation and locomotion. You will learn about new actuators and sensors, control architectures, and machine-learning methods to address robotic challenges.

Lecturer: Prof. Robert Katzschmann, D-MAVT

Occurence: Offered every Spring Semester at ETH Zurich.

Objectives

By the end of this course, students will be able to:

1. Design and Implement Soft Robotic Solutions
• Formulate functional requirements for a given challenge,
• Select appropriate soft actuator materials,
• Devise a fabrication approach,
• Develop a basic control strategy.

2. Model and Control Highly Articulated Robots in Real-Life Scenarios
• Identify dynamic skills needed for realistic applications,
• Combine suitable multiphysics modeling, control, and learning techniques,
• Evaluate and refine these models/controls iteratively,
• Incorporate learning approaches for complex robotic tasks.

3. Apply Mechanical Impedance and Embodied Intelligence to Soft Robot Design
• Distinguish the moving elements of a given application,
• Select and configure degrees of freedom (passive or active) for the target task,
• Integrate actuation materials to create an effective body/brain synergy,
• Rapidly iterate through simulation and physical prototyping until basic functionality is achieved,
• Validate for improved performance in real-world conditions.

4. Explore Biohybrid Approaches for Robotics
• Identify problems potentially solvable with living materials,
• Examine prior work on biohybrid solutions,
• Hypothesize a new living-material approach,
• Build a minimal viable prototype that demonstrates the idea.

Content

Students will study the latest advances in materials, fabrication, modeling, and ML methods to design, simulate, and control soft and biohybrid robots. Topics include:

1. Functional and Intelligent Materials: Overviews of soft polymers, tissue engineering, and biomimetic materials used in robotic actuators.

2. Soft Actuator and Sensor Design: Morphologies of elastic actuators and sensors, with strategies for robust integration.

3. Fabrication Techniques: 3D printing, casting, roll-to-roll processes, tissue culture methods for biohybrid assemblies.

4. Biohybrid Robotics: Micro- and macrorobots made with living tissues, focusing on cell-laden scaffolds and neuromuscular actuation.

5. Modeling Approaches for Soft Robotics: Minimal-parameter models, finite-element methods, and ML-based simulations for predicting soft-body behavior.

6. Control Approaches for Soft Robotics: Exploiting robot impedance and material properties for locomotion and manipulation, including adaptive control.

7. Machine Learning for Soft Robotics: Design automation, model calibration, and control policies via data-driven approaches.

Regular assignments throughout the semester will introduce practical tools and techniques, ensuring that students gain hands-on experience in implementing these concepts.

Lecture Notes

Slides, recordings, assignments, preliminary readings, and tutorials will be available on the course’s Moodle page.

Literature

1. Yasa et al. “An Overview of Soft Robotics.” Annual Review of Control, Robotics, and Autonomous Systems 6 (2023): 1–29.

2. Polygerinos et al. “Soft Robotics: Review of Fluid‐Driven Intrinsically Soft Devices; Manufacturing, Sensing, Control, and Applications in Human‐Robot Interaction.” Advanced Engineering Materials 19.12 (2017): 1700016.

3. Cianchetti et al. “Biomedical Applications of Soft Robotics.” Nature Reviews Materials 3.6 (2018): 143–153.

4. Ricotti et al. “Biohybrid Actuators for Robotics: A Review of Devices Actuated by Living Cells.” Science Robotics 2.12 (2017).

5. Sun et al. “Biohybrid Robotics with Living Cell Actuation.” Chemical Society Reviews 49.12 (2020): 4043–4069.

Prerequisites / Notice

• Intended for Master’s and PhD students.

• Enrollment priority is given to students in the Robotics, Systems, and Control Master’s program.

• Three assignments will be given during the semester, collectively contributing 50% of the final grade.

• A computer-based written exam will take place in the final week of the spring semester, accounting for the remaining 50%.

More course details can be found on ETH's Course Catalogue for Spring 2026.