Institute of Robotics and Intelligent Systems D-MAVT

Open Opportunities

Gamification for At-Home Therapy – What app features keep people engaged? ETH Zurich Rehabilitation Engineering Lab This project explores how gamification elements in smartphone applications can keep patients engaged during unsupervised at-home physical therapy. Integrated with the upper-limb rehabilitation robotic device ReHandyBot, the RehabCoach app currently tracks basic metrics like time spent with the device and exercise levels. However, to improve adherence and motivation, this project aims to redesign the feedback system, incorporating features such as badges and personalized progress visualizations. By enhancing these features, the goal is to support patients in staying engaged and committed to their therapy regimens, ultimately improving long-term outcomes. Behavioural and Cognitive Sciences, Engineering and Technology, Information, Computing and Communication Sciences, Medical and Health Sciences Bachelor Thesis, ETH Zurich (ETHZ), Internship, Master Thesis, Semester Project

Control of initial spatial organization of microbial communities ETH Zurich Multiscale Robotics Lab We are looking for a highly motivated Master student to perform the Master thesis in a collaborative project between the Multi-Scale Robotics Lab (Dr. Minghan Hu, D-MAVT) and Laboratory for Soft Materials and Interfaces (Prof. Lucio Isa & Dr. Jeremy Wong, D-MATL) at ETH Zurich. By encapsulating bacteria in microcapsules and organizing them in specific patterns, we aim to understand how these communities form and behave. This research offers an exciting opportunity to explore the dynamics of bacterial communities and contribute to the development of innovative biotechnological methods. Biomedical Engineering, Interdisciplinary Engineering, Materials Engineering, Medical Microbiology, Microbiology ETH Zurich (ETHZ), Master Thesis

Self-locking cuff electrodes for the nerve stimulation ETH Zurich Multiscale Robotics Lab Nerve cuff electrodes are designed for reliable recording and stimulation of peripheral nerves, as illustrated in Figure A. This project aims to develop a wireless, self-locking cuff electrode tailored specifically for nerve stimulation, as depicted in Figure B. The student will determine a suitable biocompatible material for the electrode, design the electrode structure, and optimize its curvature using both theoretical analysis and finite element method (FEM) simulations to enhance the electrode's ability to wrap around the nerve. The student will also explore the electrode's application in stimulation in the central nervous system (CNS) and the peripheral nervous system (PNS) and investigate other potential biomedical applications. Biomaterials, Biomechanical Engineering, Mechanical Engineering Master Thesis, Semester Project

System integration and automation of 3D printer for smart materials printing ETH Zurich Multiscale Robotics Lab Stereolithography (SLA) 3D printing technology offers high speed and resolution for printing smart materials that respond to external stimuli such as light, ultrasound, and magnetic fields. We have developed an SLA 3D printer equipped with a control interface implemented in Python within the ROS framework. The student will work on improving the current printer design by integrating a rotation platform to enable a third degree of freedom in printing. Additionally, the student will modify the control interface to achieve full automation of the printing process. The student will also characterize the final printing performance. Demonstrations will be designed to highlight the advantages of this enhanced 3D printer. (Don’t you want to make this cute 2D Pikachu become 3D alive?!) Electrical Engineering, Mechanical Engineering, Printing Technology Bachelor Thesis, Semester Project

GPU Accelerated Collision-Free Path-Planning for Robot Arm Control ETH Zurich Robotic Systems Lab Improve collision-free path planning for robotic arms used for waste sorting by leveraging the CUDA-accelerated parallel planning library cuRobo. Engineering and Technology Master Thesis, Semester Project

LIDAR Odometry and State Estimation for Space Robotics applications ETH Zurich Autonomous Systems Lab Together with ESA and Beyond Gravity, we're developing a system for testing microgravity space structures on earth. To do so, we're developing reactive ground robots that are able to support e.g. solar panels while their unfolding from a satellite is tested. Your part of the project is to develop, evaluate and test (on the robot) state estimation solutions based on LIDAR and IMU. Intelligent Robotics Semester Project

Reinforcement Learning for Excavation Planning ETH Zurich Robotic Systems Lab We aim to develop a reinforcement learning-based global excavation planner that can plan for the long term and execute a wide range of excavation geometries. The system will be deployed on our legged excavator. Intelligent Robotics Master Thesis, Semester Project

Foundation models for vision-based reinforcement learning University of Zurich Robotics and Perception Vision-based reinforcement learning (RL) is more sample inefficient and more complex to train compared to state-based RL because the policy is learned directly from raw image pixels rather than from the robot state. In comparison to state-based RL, vision-based policies need to learn some form of visual perception or image understanding from scratch, which makes them way more complex to learn and to generalise. Foundation models trained on vast datasets have shown promising potential in outputting feature representations that are useful for a large variety of downstream tasks. In this project, we investigate the capabilities of such models to provide robust feature representations for learning control policies. We plan to study how different feature representations affect the exploration behavior of RL policies, the resulting sample complexity and the generalisation and robustness to out-of-distribution samples. Intelligent Robotics Master Thesis, Semester Project

Sim-to-real transfer of event-camera-based RL policies University of Zurich Robotics and Perception This project aims to develop and evaluate drone navigation policies using event-camera inputs, focusing on the challenges of transferring these policies from simulated environments to the real world. Event cameras, known for their high temporal resolution and dynamic range, offer unique advantages over traditional frame-based cameras, particularly in high-speed and low-light conditions. However, the sim-to-real gap—differences between simulated environments and the real world—poses significant challenges for the direct application of learned policies. In this project we will look try to understand the sim-to-real gap for event cameras and how this gap influences downstream control tasks, such as flying in the dark, dynamic obstacle avoidance and, object catching. Intelligent Robotics, Robotics and Mechatronics Master Thesis

Offline-to-Online (model-based) Reinforcement Learning Transfer and Finetuning for Vision-based Robot Control University of Zurich Robotics and Perception Vision-based reinforcement learning (RL) is often sample-inefficient and computationally very expensive. One way to bootstrap the learning process is to leverage offline interaction data. However, this approach faces significant challenges, including out-of-distribution (OOD) generalization and neural network plasticity. The goal of this project is to explore methods for transferring offline policies to the online regime in a way that alleviates the OOD problem. By initially training the robot's policies system offline, the project seeks to leverage the knowledge of existing robot interaction data to bootstrap the learning of new policies. The focus is on overcoming domain shift problems and exploring innovative ways to fine-tune the model and policy using online interactions, effectively bridging the gap between offline and online learning. This advancement would enable us to efficiently leverage offline data (e.g. from human or expert agent demonstrations or previous experiments) for training vision-based robotic policies. Intelligent Robotics, Robotics and Mechatronics Master Thesis