 Robotic Systems LabOpen OpportunitiesIn 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
| This project explores unsupervised learning using extensive videos from the internat that capture human interactions with objects. By harnessing advanced generative AI models, the focus is on understanding object affordances, such as identifying interaction points and predicting post-grasp trajectories. - Computer Vision, Intelligent Robotics
- ETH Zurich (ETHZ), Master Thesis, Semester Project
| We aim to develop a method to incorporate fine-grained tactile and visual feedback into our haptic teleoperation setup and investigate their effectiveness with state-of-the-art imitation learning methods. - Intelligent Robotics
- Master Thesis, Semester Project
| Minimal is a mostly 3D-printed, highly reconfigurable robot. Using state-of-the-art reinforcement learning, we will explore novel and highly advanced hardware design possibilities that will be coupled with design optimization through learning. This will enable the next generation of robots to be a lot faster, stronger and agile. - Engineering and Technology
- Master Thesis, Semester Project
| Searching for specific objects within a confined space requires advanced spatial and perceptual reasoning. In this project, we want to develop a learning-based system that finds a desired object in a cluttered scene efficiently and safely. - Computer Vision, Knowledge Representation and Machine Learning, Learning, Memory, Cognition and Language
- Master Thesis
| Model-based state estimation for locomotion has shown some significant drawbacks, especially in the case of complex contact scenarios. At the same time, locomotion controllers are evolving, now purposely using knee contacts or wheel slippage for advanced motions. The current model-based state estimation techniques often cannot supply sufficiently accurate observations for these controllers, leading to major estimation drifts and thus potential failures. In this project, we aim to leverage learning-based methods not only for locomotion control, but also for state estimation. Preliminary work shows that creating a state estimation through supervised learning from recorded simulation data can produce a viable solution. Furthermore, fusing these approaches with classical filtering theory opens a promising realm of research. The project should also compare the developed methods with existing approaches on real hardware. If time permits, we are interested in learning state estimation and locomotion jointly. - Intelligent Robotics, Robotics and Mechatronics
- Master Thesis, Semester Project
| Robots, like humans, should be able to use different parts of their morphology (base, elbow, hips, feet) for interaction. This project focuses on learning multi-modal interactions from demonstrations for mobile manipulators. - Intelligent Robotics, Knowledge Representation and Machine Learning
- Master Thesis, Semester Project
| This project addresses the task of 6D pose estimation for general-purpose objects, particularly when dealing with occlusion. We aim to leverage recent deep learning methods and synthetic data generation schemes to enable robust object manipulation. - Intelligent Robotics
- Master Thesis, Semester Project
| This project explores wheeled-legged legged robots, i.e., a robot that has both wheels and point-feet as end-effectors of its legs. Thereby, different locomotion modes should be explored, as well as different configurations of mounting wheels to legs. One idea could be a diagonal bicycle mode, another could be optimizing locomotion for payload transport. The project should include the implementation and deployment of the developed locomotion concepts and policies on real hardware. - Intelligent Robotics, Robotics and Mechatronics
- Master Thesis, Semester Project
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Transport of packages of various dimensions is often mentioned as one of the most viable use cases for autonomous mobile robots. The ability to autonomously pick up and self-load a package is, however, a functionality that many systems are still lacking. Preliminary work showed that quadrupedal robots have the potential to execute this skill by manipulating payloads with their legs or main body. In this project, we aim to investigate how legged and wheeled legged robots can achieve autonomous package pick-and-load tasks with practical design modifications and clever maneuvers.
- Intelligent Robotics, Mechanical Engineering, Robotics and Mechatronics
- Master Thesis, Semester Project
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