Student Projects
π-petter: Dexterous Lab Automation, Teaching Robots to Pipette
Most labs rely on certified, human-operated processes. Fully automating them from scratch triggers costly re-certification — so the smarter path is to replicate human steps with a robot using the same tools on the same benchtop. Pipetting is one of the most frequent and critical wet-lab tasks: precise, repetitive, and highly sensitive to technique. This thesis builds a robot capable of picking up a standard pipetter, aspirating liquid, dispensing a defined volume repeatedly, and returning the tool — using simulation, foundation models, and sim-to-real transfer rather than hand-crafted automation, making the solution generalizable without re-engineering.
Keywords
Dexterous Lab Automation World Models
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2026-05-08 , Earliest start: 2026-08-03 , Latest end: 2027-02-26
Applications limited to Department of Computer Science , Department of Mechanical and Process Engineering
Organization Mobile Robotics Lab
Hosts Iovino Matteo , Schiavi Giulio
Topics Information, Computing and Communication Sciences
Map Quality Evaluation for Reliable Visual SLAM
*Background* Visual SLAM enables mobile robots to localize and build maps of previously unseen environments. In industrial deployments, map quality [1, 2] strongly affects long-term localization robustness and fleet-level performance. A high-quality map is necessary to ensure reliable performance over months of operation and across hundreds of robots. Today, there are no reliable methods to automatically assess a map quality, and manual inspections are still required. Developing a systematic and automated evaluation pipeline would greatly improve fleet monitoring, localization robustness map validation, and in general deepen the understanding of SLAM maps. *Objective* This master thesis aims to establish a principled understanding of what constitutes a high-quality SLAM map and to develop quantitative metrics that capture map reliability and uncertainty. We will design and implement an evaluation pipeline capable of assessing map quality along multiple dimensions, including: -- Global and local consistency: detecting large-scale drift / distortion, but also identify noisy area, with accumulation of outlier landmarks -- Localizability: measuring how effectively a robot can relocalize in different parts of the map. -- Temporal stability: analyzing how map quality evolves as the environment is updated over time. The final outcome will be an integrated map-quality evaluation module embedded within a state-of-the-art commercial SLAM system deployed across hundreds of robots. This module will support both offline map inspection and real-time monitoring.
Keywords
SLAM, Computer Vision, Sevensense, ABB
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Master Thesis , ETH Zurich (ETHZ)
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Published since: 2026-03-11 , Earliest start: 2026-03-15 , Latest end: 2026-09-01
Applications limited to ETH Zurich , EPFL - Ecole Polytechnique Fédérale de Lausanne
Organization RobotX Center
Hosts Cadena Cesar , Ott Lionel
Topics Information, Computing and Communication Sciences , Engineering and Technology
Note on plagiarism
We would like to suggest every student, irrespective of the type of project (Bachelor, Semester, Master, ...), to make himself/herself familiar with ETH rules regarding plagiarism