Student Projects

We offer student projects such as bachelor theses, semester projects or master theses and we are also open for students' own proposals on potential students projects.

ETH Zurich uses SiROP to publish and search scientific projects. For more information visit sirop.org.

Development of a Giant Humanoid Robotic Hand

Development of a Giant Humanoid Robotic Hand for the rapid deployment and handling of equipment in flooding and earthquake disaster scenarios

Keywords

Soft Robotics, Humanoid Robotic Hand, Tendon driven, Dexterous manipulator, Rolling contact joints

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Semester Project , Master Thesis

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Published since: 2025-01-06 , Earliest start: 2025-01-06 , Latest end: 2025-08-31

Organization Soft Robotics Lab

Hosts Hinchet Ronan , Katzschmann Robert, Prof. Dr.

Topics Engineering and Technology

Development of sensor networks integrated on scaly artificial robotic skins

Development of a multi-sensory network integrated on artificial scales serving as soft robotic skin for robotic limbs

Keywords

Soft Robotics, robotic skin, sensor network, artificial scale, sensory scales

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Semester Project , Master Thesis

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Published since: 2025-01-06 , Earliest start: 2025-01-06 , Latest end: 2025-08-31

Organization Soft Robotics Lab

Hosts Hinchet Ronan , Katzschmann Robert, Prof. Dr.

Topics Engineering and Technology

Assembly Assistant: Crafting your robotic companion for teamwork

This project is inspired by the vision of seamless human-robot collaboration in household settings. As our homes become smarter, the need for robotic systems that can work alongside humans to perform tasks with precision and adaptability is growing. This project empowers individuals to design and build a robotic companion tailored to assist with household tasks like assembling furniture. By fostering teamwork between humans and robots, the project highlights how technology can enhance everyday life, promoting efficiency, creativity, and a shared sense of accomplishment. It envisions a future where robots are not just tools but collaborative partners, making home life easier, more productive, and more enjoyable for everyone.

Keywords

human-robot collaboration, egocentric vision, dexterous manipulation

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Semester Project , Master Thesis , ETH Zurich (ETHZ)

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Published since: 2025-01-06 , Earliest start: 2025-01-13

Organization Computer Vision and Geometry Group

Hosts Wang Xi , Gavryushin Alexey , Yang Chenyu

Topics Information, Computing and Communication Sciences

Enhancing performance of electrostatic rotational motors for the next generation of robotic actuators

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.

Keywords

soft robotics, electrostatic motors, electrostatic actuators, mechanical design

Labels

Master Thesis

Description

Electrostatic actuators have the potential to be a far more efficient alternative to conventional motors based on the electromagnetic principle, as they can sustain torque output without consuming energy, while still being compliant and backdrivable. Based on an existing design for an electrostatic motor using artificial muscles, you will propose and implement solutions to improve its specific power (i.e. lighter) and power density (i.e. smaller), as well as streamline the manufacturing procedure. You can approach it from different perspectives such as mechanical (e.g. improve layout of the structure), materials (e.g. use a lighter / more robust material for the mechanism), or optimization-based (e.g. FEM simulations to optimize the structure).

Requirements:

  • Willingness to prototype quickly and iterate through many versions

  • Strong motivation, problem-solving skills, and ability to work independently

  • Proficiency in CAD / simulation software to create and validate designs

References:

  1. Ludois, Daniel C., Kevin J. Frankforter, Baoyun Ge, Aditya N. Ghule, Peter Killeen, and Ryan P. Knippel. 2022. “Macroscale Electrostatic Rotating Machines and Drives: A Review and Multiplicative Gain Performance Strategy.” IEEE Journal of Emerging and Selected Topics in Power Electronics 10 (1): 14–34.

Goal

Work Packages:

  1. Review literature on electrostatic actuation and rotational mechanisms

  2. Compare different approaches to improve the performance of the existing electrostatic motor

  3. Strategically research & develop the improvements to the motor.

  4. Implement a working prototype

  5. Quantify how much each metric has improved over the original

Contact Details

To apply, please send Yasunori a short motivation statement for this project, with a copy of your CV, transcripts, and two reference contacts if you have worked on any past projects. Also feel free to contact me for any questions!

Yasunori Toshimitsu, PhD Candidate, ytoshimitsu@ethz.ch, Soft Robotics Lab, D-MAVT, ETH Zurich

Amirhossein Kazemipour, PhD Candidate, akazemi@ethz.ch, Soft Robotics Lab, D-MAVT, ETH Zurich

Prof. Robert Katzschmann, Assistant Professor of Robotics, rkk@ethz.ch, Soft Robotics Lab, Institute of Robotics and Intelligent Systems, D-MAVT, ETH Zurich

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Published since: 2025-01-05 , Earliest start: 2025-01-06 , Latest end: 2025-09-01

Organization Soft Robotics Lab

Hosts Katzschmann Robert, Prof. Dr. , Toshimitsu Yasunori , Kazemipour Amirhossein

Topics Engineering and Technology

Development of Neuromuscular Biohybrid Robots

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.

Keywords

Tissue engineering, mechanical engineering, biology, neuroengineering, biomaterials, biohybrid robotics, 3D in vitro models, biofabrication, bioprinting, volumetric printing.

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Semester Project , Bachelor Thesis , Master Thesis , ETH Zurich (ETHZ)

Description

Our project focuses on overcoming current limitations in neuromuscular biohybrid robots, particularly scaffold design and the development of functional neuromuscular junctions (NMJs). You will apply expertise in biology, biomaterial synthesis, and biofabrication to tackle the key challenges of creating functional engineered tissues within the field of biohybrid robotics.

Goal

By incorporating moto-neurospheres into 3D co-culture systems with skeletal muscle tissues, we aim to establish global innervation and enable synchronized muscle contractions.

Contact Details

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Published since: 2024-12-17 , Earliest start: 2025-01-15

Organization Soft Robotics Lab

Hosts Katzschmann Robert, Prof. Dr.

Topics Engineering and Technology , Biology

Deep Learning of Residual Physics For Soft Robot Simulation

Incorporating state-of-the-art deep learning approaches to augment conventional soft robotic simulations for a fast, accurate and useful simulation for real soft robots.

Keywords

Soft Robotics, Machine Learning, Physical Modeling, Simulation

Labels

Semester Project , Master Thesis

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Published since: 2024-12-01 , Earliest start: 2024-08-01 , Latest end: 2025-06-30

Organization Soft Robotics Lab

Hosts Michelis Mike , Katzschmann Robert, Prof. Dr.

Topics Information, Computing and Communication Sciences , Engineering and Technology

GPU Acceleration of Soft Body Modeling: Enhancing Performance with CUDA

The Soft Robotics Lab is developing a GPU-accelerated soft body modeling framework using the Finite Element Method (FEM). This enhancement aims to improve computational efficiency and enable more complex, real-time simulations. By leveraging GPUs' parallel processing power, simulations will be significantly faster. The project seeks to advance soft robotics research and enable innovative applications.

Keywords

Soft Body Simulation, high-performance computing, GPU programming, Parallel Computing, Finite Element Method (FEM), Multiphysics Simulation

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Semester Project , Bachelor Thesis , Master Thesis

Description

The Soft Robotics Lab is developing a framework for soft body modeling using the Finite Element Method (FEM) and an energy minimization model. The current implementation is optimized for parallel execution on CPUs. To meet the demand for more complex simulations and improve computational efficiency, we propose developing a GPU-accelerated version of the framework. By harnessing the computational power of GPUs, we aim to significantly speed up simulations and scale up problem sizes, enabling more detailed and realistic soft robotics simulations as well as real-time control. GPUs excel in handling numerous parallel computations simultaneously, making them ideal for speeding up iterative calculations inherent in soft body dynamics simulations. Ultimately, this effort contributes to pushing the boundaries of soft robotics research and application, paving the way for innovative developments in the field.

Work Packages

  • Understand the current soft body modeling framework and its CPU parallelization.
  • Review literature on CUDA and GPU acceleration for FEM.
  • Set up a development environment for GPU programming.
  • Fine-tune CUDA kernels and parallelization strategies for maximum performance.
  • Conduct benchmark simulations to verify correctness and performance gains.

Requirements

  • Strong academic background with exceptional grades in computer science.
  • Strong Python and C++ programming skills.
  • Knowledge in FEM-based simulations.
  • Knowledge in basic GPU Programming including CUDA.
  • Keen to learn more about soft robotics and physical modeling.
  • Capable of both working independently and cooperating with mentors and teammates.

Contact Details

Manuel Mekkattu, manuel.mekkattu@srl.ethz.ch, Soft Robotics Lab, D-MAVT, ETH Zürich

Mike Michelis, michelism@ethz.ch, Soft Robotics Lab, D-MAVT, ETH Zürich

Prof. Robert Katzschmann, rkk@ethz.ch, Soft Robotics Lab, D-MAVT, ETH Zürich

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Published since: 2024-11-25 , Earliest start: 2024-08-01

Organization Soft Robotics Lab

Hosts Mekkattu Manuel , Katzschmann Robert, Prof. Dr.

Topics Information, Computing and Communication Sciences , Engineering and Technology , Physics

Transfer Learning with Inpainting: Learn hand manipulation skills with human hand demonstrations

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.

Keywords

dexterous hand manipulation, transfer learning, cross-embodiment

Labels

Semester Project , Bachelor Thesis , Master Thesis

Description

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.

Work packages

  • Literature research
  • Implement inpainting augmentation
  • Imitation learning policy training
  • Hardware deployment

Requirements

  • Strong programming skills in Python
  • Experience in reinforcement learning

Publication

This project will mostly focus on algorithm design and system integration. Promising results will be submitted to robotics and machine learning conferences where outstanding robotic performances are highlighted.

Related literature

  • Lawrence Yunliang Chen, Kush Hari, Karthik Dharmarajan, Chenfeng Xu, Quan Vuong, Ken Goldberg: Mirage: Cross-Embodiment Zero-Shot Policy Transfer with Cross-Painting. CoRR abs/2402.19249 (2024)
  • Zoey Qiuyu Chen, Shosuke C. Kiami, Abhishek Gupta, Vikash Kumar: GenAug: Retargeting behaviors to unseen situations via Generative Augmentation. Robotics: Science and Systems 2023
  • Lawrence Yunliang Chen, Chenfeng Xu etal: RoVi-Aug: Robot and Viewpoint Augmentation for Cross-Embodiment Robot Learning
  • Davide Liconti, Yasunori Toshimitsu, Robert K. Katzschmann: Leveraging Pretrained Latent Representations for Few-Shot Imitation Learning on a Dexterous Robotic Hand. CoRR abs/2404.16483 (2024)

Contact Details

Chenyu Yang: chenyu.yang@srl.ethz.ch

Liconti Davide: davide.liconti@srl.ethz.ch

To apply, please send to both the contacts a short motivation statement for this project, with a copy of your CV, transcripts, and two reference contacts if you have worked on any past projects.

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Published since: 2024-11-19 , Earliest start: 2024-12-01 , Latest end: 2025-12-31

Applications limited to University of Zurich , ETH Zurich

Organization Soft Robotics Lab

Hosts Liconti Davide , Yang Chenyu

Topics Information, Computing and Communication Sciences

Exploring Morphology-Agnostic Control for Dexterous Robotic Manipulation

Reinforcement learning (RL) has shown promising results in enabling robust, adaptive control in robotics. However, these policies work for specific robot embodiments. This project aims to adapt a morphology-agnostic control framework to the domain of dexterous robotic manipulation. By leveraging techniques like morphology-agnostic encoders and Body Transformer architectures, the student will investigate how to train a single control policy that can handle diverse robotic hand embodiments, enabling flexible and transferable control for dexterous tasks.

Keywords

Dexterous manipulation, morphology-agnostic control, reinforcement learning, robotic hands,

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Semester Project , Master Thesis

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This project is set to limited visibility by its publisher. To see the project description you need to log in at SiROP. Please follow these instructions:

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Published since: 2024-11-16 , Earliest start: 2024-12-01 , Latest end: 2025-12-31

Applications limited to ETH Zurich , Institute of Robotics and Intelligent Systems D-HEST

Organization Soft Robotics Lab

Hosts Yang Chenyu , Toshimitsu Yasunori

Topics Information, Computing and Communication Sciences

Computational Modeling of Artificial Muscle Cells for Biohybrid Robots

This 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.

Keywords

Biohybrid Robotics, Computational Models, Soft Body Simulation, Machine Learning, Surrogate Models, Finite Element Method (FEM)

Labels

Semester Project , Bachelor Thesis , Master Thesis

Description

Are you a highly motivated and academically outstanding Bachelor's or Master's student? Are you passionate about exploring the intersection of physics, computer science, robotics, and biology? We are looking for students to join the Soft Robotics Lab at ETH Zurich for a Semester, Bachelor’s or Master’s Project and contribute to groundbreaking research in the field of biohybrid robotics. The objective in biohybrid robotics is to combine living biological components with artificial materials to create adaptable and functional systems capable of performing various tasks. As a part of our research group, you will be working on the development of computational models for muscle cells, a crucial element in the creation of biohybrid robots. Modeling the intricate structure and behavior of biological muscles poses a challenge due to their complex and nonlinear characteristics. Simulation models must capture the biomechanical and physiological aspects of muscle function, including factors like elasticity or muscle fatigue. Furthermore, artificial muscles exist within a fluidic environment, introducing the relevance of fluid-structure interaction (FSI). This also highlights the importance of the dynamic interplay between the deformable structures of the muscles and the surrounding fluid, further complicating the modeling and control aspects in biohybrid robotic systems. Our research involves leveraging both first principle soft body simulation methods and machine learning techniques to enhance our understanding and control of biohybrid systems.

Work Packages

Possible work packages could be:

  1. Soft Body Simulation Methods: Engage in the development and refinement of first principle models for simulating the behavior of muscle cells within the context of biohybrid robots. Conduct literature review of existing work in the field of muscle modeling.
  2. Machine Learning Surrogate Models: Contribute to the integration of machine learning approaches for creating accurate surrogate models, enabling efficient and real-time control of biohybrid systems.

Requirements

  • Strong academic background with exceptional grades in physics, computer science, mechanical engineering, biomedical engineering, or related fields.
  • Strong Python programming skills, ideally also C++.
  • Proficiency in FEM-based simulations.
  • Prior knowledge in muscle biology and machine learning is a plus.
  • Enthusiasm for interdisciplinary research and a keen interest in soft robotics.
  • Capable of both working independently and cooperating with mentors and teammates.

Contact Details

Manuel Mekkattu, manuel.mekkattu@srl.ethz.ch, Soft Robotics Lab, D-MAVT, ETH Zürich

Prof. Robert Katzschmann, rkk@ethz.ch, Soft Robotics Lab, D-MAVT, ETH Zürich

If you are interested, please submit a motivation letter, your CV, transcripts, and two references.

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Published since: 2024-11-07 , Earliest start: 2024-08-01

Organization Soft Robotics Lab

Hosts Katzschmann Robert, Prof. Dr. , Mekkattu Manuel

Topics Information, Computing and Communication Sciences , Engineering and Technology , Biology , Physics

Development of a linear electrostatic film actuator for a Humanoid Robotic Hand

Development of a linear electrostatic film actuator for soft robotic applications such as the actuation of a humanoid robotic hand.

Keywords

Electrostatic, Linear actuator, Flexible electronics, Soft Robotics, Humanoid Robotic Hands

Labels

Master Thesis

Description

We are seeking a motivated student to develop linear electrostatic film actuators for soft robotic applications. Electrostatic film actuators are mechanism based on the electrostatic force generated when applying a voltage between two close electrodes forming a capacitor. The force and direction can be controlled using multi-phase signals. The thin, light and flexible form factor of such actuator technology is key for soft robotics. Such actuators have been already demonstrated for actuating flexible fish robots and wall climbing robots.

The linear electrostatic film actuators will be placed in an artificial forearm and used to move the finger’s joints of a humanoid robotic hand. Using such actuator in a humanoid robotic hand could have many advantages over standard electromagnetic motors, notably it could improve energy efficiency while being lighter, smaller and flexible which will be key for future robots.

Your task will be to investigate, design and manufacture a prototype of linear electrostatic film actuator and to characterize it. This would constitute the first prototype of such actuator at ETHZ. If successful, the actuators will be integrated and tested in a new generation of humanoid robotic hand.

This project is suitable for students with a strong background in electronic design, dielectric materials and thin-film manufacturing, and ideally electrostatic physics. The project will require a combination of design, hands-on work, as well as theoretical or computational analysis. If you are interested in this project and believe you have the skills and motivation to contribute, we encourage you to apply.

Work Packages

  1. Conduct a literature review on electrostatic film actuator technology to determine the best designs for a first prototype.
  2. Design and fabricate a working prototype of such actuator in the Soft Robotic Lab.
  3. Characterize the actuator and compare its performance to literature.
  4. Optimize the prototype design and fabrication to improve its performance
  5. Integrate the actuator in the humanoid robotic hand of the Soft Robotics Lab.

Requirements

▪ High motivation, problem-solving ability ▪ Experience in electronic circuit design ▪ Experience in flexible PCB ▪ Previous experience with dielectric materials and their thin film manufacturing is a plus

Contact Details

Dr. Ronan Hinchet, ronan.hinchet@srl.ethz.ch, Institute of Robotics and Intelligent Systems, D-MAVT Amirhossein Kazemipour, akazemi@ethz.ch, Institute of Robotics and Intelligent Systems, D-MAVT Prof. Robert Katzschmann, rkk@ethz.ch, Institute of Robotics and Intelligent Systems, D-MAVT

If you are interested, please submit a short motivational statement, your CV, transcripts, and one to two reference contacts. Applications via Sirop: https://srl.ethz.ch/education/student-projects.html.

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Published since: 2024-11-06 , Earliest start: 2024-11-15 , Latest end: 2025-08-31

Organization Soft Robotics Lab

Hosts Hinchet Ronan , Kazemipour Amirhossein , Katzschmann Robert, Prof. Dr.

Topics Engineering and Technology

For all projects, please contact the responsible supervisor if you have questions and apply via sirop.org with your cover letter, detailed CV, transcripts, and prior publications (if you have any).

In case you have project ideas related to our research areas or research platforms, take the opportunity and propose your own project!