 Robotic Systems LabOpen OpportunitiesTLDR: Improving navigation capabilities of ANYmal - RL is simulation - optimizing learning progress. - Computer Hardware, Computer Perception, Memory and Attention, Computer Vision, Electrical Engineering, Intelligent Robotics, Robotics and Mechatronics
- Master Thesis, Semester Project
| This project uses Visual Language Models (VLMs) for high-level planning and supervision in construction tasks, enabling task prioritization, dynamic adaptation, and multi-robot collaboration for excavation and site management.
prioritization, dynamic adaptation, and multi-robot collaboration for excavation and site management - Information, Computing and Communication Sciences
- Master Thesis, Semester Project
| Recent advancements in AI, particularly with models like Claude 3.7 Sonnet, have showcased enhanced reasoning capabilities. This project aims to harness such models for excavation planning tasks, drawing parallels from complex automation scenarios in games like Factorio. We will explore the potential of these AI agents to plan and optimize excavation processes, transitioning from simulated environments to real-world applications with our excavator robot. - Engineering and Technology
- Master Thesis, Semester Project
| Enable Birds-Eye-View perception on autonomous mobile robots for human-like navigation. - Computer Vision, Intelligent Robotics, Neural Networks, Genetic Alogrithms and Fuzzy Logic, Pattern Recognition, Photogrammetry and Remote Sensing
- ETH Zurich (ETHZ), Master Thesis
| Elevate semantic scene graphs to a new level and perform semantically-guided navigation and interaction with real robots at The AI Institute. - Computer Vision, Engineering and Technology, Intelligent Robotics, Neural Networks, Genetic Alogrithms and Fuzzy Logic, Pattern Recognition
- ETH Zurich (ETHZ), Master Thesis
| In this thesis, you will contribute to the Autonomous River Cleanup (ARC) by helping develop SARA, a bridge-mounted, camera-based system for monitoring river waste. Your focus will be on modeling the system’s power dynamics to determine the ideal battery and solar panel size, and balancing runtime throughout the day with overall the system size and weight. If time allows, you will also validate your findings with tests on the real hardware. - Engineering and Technology
- Bachelor Thesis, Semester Project
| In this thesis, you will work on SARA, a bridge-mounted, smartphone-based system for detecting and monitoring river waste. The focus will be on selecting lightweight detection and classification models suitable for smartphones and exploring domain adaptation techniques to improve performance across different locations with minimal retraining. Your work will build on previous research at ARC and current literature to develop solutions that balance model robustness and computational efficiency. - Engineering and Technology
- Semester Project
| In this thesis, you will contribute to the Autonomous River Cleanup (ARC) by helping improve MARC, our robotic platform for autonomous waste sorting. Your work will focus on optimizing the robot arm configuration by simulating different base locations and degrees of freedom to achieve faster and more efficient pick-and-place movements in a confined space. You will build on our existing simulation environment to model and evaluate various setups. - Engineering and Technology
- Semester Project
| The Autonomous River Cleanup (ARC) is developing SARA, the next iteration of a bridge-mounted, camera-based system to detect and measure riverine waste. Smartphones offer a compact, affordable, and powerful core for year-round monitoring but are vulnerable to shutdowns from extreme heat in summer and cold in winter. This thesis focuses on assessing these thermal challenges and designing protective solutions to ensure reliable, continuous operation. - Engineering and Technology
- Bachelor Thesis, Semester Project
| This thesis, part of the Autonomous River Cleanup (ARC) initiative in collaboration with The SeaCleaners, explores adaptive computer vision methods for automated quantification of oceanic plastic waste on the Mobula 10 vessel. The work focuses on applying continual learning and domain adaptation techniques to improve a baseline detection model’s robustness to changing waste types and environments. The system will be evaluated in real-world conditions to assess its performance and guide future research in environmental monitoring. - Engineering and Technology
- Master Thesis
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