Institute of Robotics and Intelligent Systems D-HESTOpen OpportunitiesStudy State-Space Models (SSMs) within the realm of Reinforcement Learning (RL) and ideally apply it in Robotics field. - Computer Vision, Intelligent Robotics, Knowledge Representation and Machine Learning
- Master Thesis
| This project focuses on utilizing various techniques for Video to Events generation. - Computer Vision
- Master Thesis
| The goal of the project is to assess the feasibility of using commercially available plantar pressure monitoring devices (so called smart insoles) on the diabetic population. Pressure ulcers are a common complication of the diabetic foot, and monitoring plantar pressure continuously is a potential measure of prevention. Diabetic patients are often prescribed personalized footwear (e.g., curved insoles that accommodate any deformity in the feet). This project aims at assessing the potential of the smart insoles available on the market to monitor plantar pressure in diabetic patients with such custom footwear. - Biomedical Engineering, Medical and Health Sciences
- Bachelor Thesis, Semester Project
| The remarkable agility of animals, characterized by their rapid, fluid movements and precise interaction with their environment, serves as an inspiration for advancements in legged robotics. Recent progress in the field has underscored the potential of learning-based methods for robot control. These methods streamline the development process by optimizing control mechanisms directly from sensory inputs to actuator outputs, often employing deep reinforcement learning (RL) algorithms. By training in simulated environments, these algorithms can develop locomotion skills that are subsequently transferred to physical robots. Although this approach has led to significant achievements in achieving robust locomotion, mimicking the wide range of agile capabilities observed in animals remains a significant challenge. Traditionally, manually crafted controllers have succeeded in replicating complex behaviors, but their development is labor-intensive and demands a high level of expertise in each specific skill. Reinforcement learning offers a promising alternative by potentially reducing the manual labor involved in controller development. However, crafting learning objectives that lead to the desired behaviors in robots also requires considerable expertise, specific to each skill.
- Information, Computing and Communication Sciences
- Master Thesis
| Humanoid robots, designed to mimic the structure and behavior of humans, have seen significant advancements in kinematics, dynamics, and control systems. Teleoperation of humanoid robots involves complex control strategies to manage bipedal locomotion, balance, and interaction with environments. Research in this area has focused on developing robots that can perform tasks in environments designed for humans, from simple object manipulation to navigating complex terrains. Reinforcement learning has emerged as a powerful method for enabling robots to learn from interactions with their environment, improving their performance over time without explicit programming for every possible scenario. In the context of humanoid robotics and teleoperation, RL can be used to optimize control policies, adapt to new tasks, and improve the efficiency and safety of human-robot interactions. Key challenges include the high dimensionality of the action space, the need for safe exploration, and the transfer of learned skills across different tasks and environments. Integrating human motion tracking with reinforcement learning on humanoid robots represents a cutting-edge area of research. This approach involves using human motion data as input to train RL models, enabling the robot to learn more natural and human-like movements. The goal is to develop systems that can not only replicate human actions in real-time but also adapt and improve their responses over time through learning. Challenges in this area include ensuring real-time performance, dealing with the variability of human motion, and maintaining stability and safety of the humanoid robot.
- Information, Computing and Communication Sciences
- Master Thesis
| In recent years, advancements in reinforcement learning have achieved remarkable success in teaching robots discrete motor skills. However, this process often involves intricate reward structuring and extensive hyperparameter adjustments for each new skill, making it a time-consuming and complex endeavor. This project proposes the development of a skill generator operating within a continuous latent space. This innovative approach contrasts with the discrete skill learning methods currently prevalent in the field. By leveraging a continuous latent space, the skill generator aims to produce a diverse range of skills without the need for individualized reward designs and hyperparameter configurations for each skill. This method not only simplifies the skill generation process but also promises to enhance the adaptability and efficiency of skill learning in robotics. - Engineering and Technology, Information, Computing and Communication Sciences
- Master Thesis
| The goal of the project is to develop and test a smart sock prototype for plantar pressure measurements. Existing previously developed textile pressure sensors are to be integrated in a standard sock. This technology can be used for plantar pressure monitoring in diverse wearable applications ranging from healthcare to sports. - Biomedical Engineering, Medical and Health Sciences
- Master Thesis
| Cartilage damage in the knee joint can be caused by aging or repetitive actions. It can be treated by surgically removing the damaged cartilage tissue and filling the generated defect with a precisely shaped, healthy cartilage graft. Removing the defected cartilage is commonly done with surgical curettes. We are investigating the use of laser ablation for a more precise defect preparation process. With two different lasers, we managed to obain promising results regarding cell viability in live samples. However, laser parameters such as pulse frequency and energy need to be optimized towards higher cutting efficiency. Your task will be to prepare a setup to test, optimize, and validate various parameter sets using different lasers for articular cartilage ablation. - Biomedical Engineering, Optical Physics
- Master Thesis
| gpuFlightmare: High-Performance GPU-Based Physics Simulation and Image Rendering for Flying Robots - Engineering and Technology
- Master Thesis, Semester Project
| Fly like a bird - Engineering and Technology
- Master Thesis, Semester Project
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