 Soft Robotics LabOpen OpportunitiesThis research aims to advance biohybrid robotics by integrating living biological components with artificial materials. The focus is on developing computational models for artificial muscle cells, a critical element in creating biohybrid robots. Challenges include modeling the complex and nonlinear nature of biological muscles, considering factors like elasticity and muscle fatigue, as well as accounting for fluid-structure interaction in the artificial muscle's environment. The research combines first principle soft body simulation methods and machine learning to improve understanding and control of biohybrid systems. - Biological Mathematics, Biomechanical Engineering, Biophysics, Mechanical Engineering, Modeling and Simulation, Robotics and Mechatronics, Simulation and Modelling
- Bachelor Thesis, Master Thesis, Semester Project
| We are enhancing soft robot modeling by developing a GPU-accelerated version of our FEM-based framework using CUDA. This research focuses on optimizing parallel computations to significantly speed up simulations, enabling larger problem sizes and real-time control. By improving computational efficiency, we aim to advance soft robotics research and facilitate more detailed, dynamic simulations. - Mechanical Engineering, Programming Techniques, Robotics and Mechatronics, Simulation and Modelling
- Bachelor Thesis, Master Thesis, Semester Project
| We are advancing soft robot simulation with FEM and energy-based methods to model complex, adaptive behaviors. This research entails developing the framework to support diverse designs, integrate new physics models, and optimize performance, enabling enhanced control and real-world applications of soft robots. - Mechanical Engineering, Robotics and Mechatronics, Simulation and Modelling
- Bachelor Thesis, Master Thesis, Semester Project
| This project aims to enhance an electrostatic actuator by improving its specific power and power density while optimizing its manufacturing process, through approaches such as mechanical redesign, materials innovation, or computational optimization. - Electrical Engineering, Mechanical Engineering, Robotics and Mechatronics
- Master Thesis
| Biohybrid robots integrate living cells and synthetic components to achieve motion. These systems often rely on engineered skeletal muscle tissues that contract upon electrical stimulation for actuation. Neuromuscular-powered biohybrid robots take this concept further by integrating motor neurons to induce muscle contractions, mimicking natural muscle actuation. In our lab, we are developing neuromuscular actuators using advanced 3D co-culture systems and biofabrication techniques to enable functional macro-scale biohybrid robots. - Biochemistry and Cell Biology, Biomaterials, Biomechanical Engineering, Biotechnology, Mechanical Engineering
- Bachelor Thesis, ETH Zurich (ETHZ), Master Thesis, Semester Project
| Imitation learning has demonstrated remarkable success in applications such as parallel grippers and robotic hands. However, current state-of-the-art imitation learning pipelines often depend heavily on demonstrations performed using the robot's specific embodiment.
Inpainting augmentation techniques present an exciting opportunity to overcome this limitation, enabling robots to learn from demonstrations involving other embodiments. This is particularly promising for dexterous hand manipulation, where skills can potentially be learned directly from extensive human hand datasets.
This project focuses on adapting inpainting augmentation methods to robotic hand manipulation. The goal is to integrate these techniques into our cutting-edge imitation learning framework and hardware, enabling efficient transfer learning from human demonstrations. - Intelligent Robotics
- Bachelor Thesis, Master Thesis, Semester Project
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