 Robotic Systems LabOpen OpportunitiesThe innumerable uses of rope, including climbing, sailing, circus acts, and wrangling livestock, suggest that it could be used as an universal robotic manipulator. When combined with a mobile base, e.g. a cowboy on a horse, rope manipulation can be an effective tool for all manner of robot-environment interaction challenges. In this project, we seek to realize a control framework for ANYmal such that it can use a rope launcher to manipulate various objects.
- Intelligent Robotics, Robotics and Mechatronics, Systems Theory and Control
- Master Thesis, Semester Project
| When quadrupedal animals injure their limbs, they adapt their gait to compensate for the injury. Conversely, when a quadruped robot’s leg breaks, its functionality is severely curbed, and in many cases, entirely compromised. This project will investigate the interplay of disabled/dismembered quadrupedal robot legs, locomotion performance, and control strategy, with the goal of converging on effective policies that abstract across different kinds of “injuries.” - Intelligent Robotics, Robotics and Mechatronics, Systems Theory and Control
- 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
| 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
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