We have exciting new projects, not only for SA and MA students, but also other team members looking for a fun and interesting project next to ETH obligations. If you are interested in a SA or MA please contact one of our PhDs to discuss project ideas. For other projects, check out the list of current projects below and contact one of our TAs for more information.
If you are interested in previous projects, go to our Project Report site, where you can download multiple reports of previous projects.
If you are from a different team and your team works with the B-Human code framework, make sure to check our YouTube channel for some tutorials on the framework by Filippo Martinoni.
Semester / Master Projects
- Pose detection by Image Translation [closed]
- Objects and field detection in Robocup [closed]
- Design of a Dynamic Omni-directional Kick Engine for NAO Bipedal Robots in RoboCup [closed]
If interested in a thesis please directly reach out to the supervisors mentioned in the proposals or send an email to email@example.com for general questions about projects.
This is a glance of the projects we have to deal with as members of the team. Every year new challenges arise and our job is to find solution to them exploiting our capabilities while implementing the newest technological advancements to made our robots work better and better.
- Automatic Calibration
The joints and the sensors of the robots need to be calibrated from time to time. At the moment, this process is still very manual, time-consuming and error prone. We want to implement a procedure through which the robot can autonomously calibrate itself by walking on the field.
- New Kick Implementation
During the 5 vs. 5 matches of our NAO robots it often happens that our robots need to duel with an opponent for acquiring the ball. In these cases, a side kick can be beneficial to use if the ball is stuck between our robot and the opponent, e.g. when dribbling towards the opponent. Therefore, we needed a new side kick motion, which can be executed in these situations. The motion of the kick has been first be developed in a motion simulator, then an approximate implementation has been provided. We now need to increase its robustness during real match conditions.
- Whole-Body Control for Kicking
We want to develop a robust algorithm to produce the kicking motion, able to deal with stability issues that could arise from any unwanted contact with the floor or other robots. Also, this approach could lead to an improvement in control over kicking direction.
- RL for optimal cooperative positioning
Our current logic is based on multiple state-machines addressing different game conditions via predefined transitions. It is reliable, but not powerful enough when a multi-robot cooperation is needed. We want to train RL agents to enable our robots to position themselves correctly in each game situation. A main RL framework for single agents and fully observable MDP has been already developed, we need to improve it for multi-agent problems. After positioning, gameplay will be addressed.
- Semantic Segmentation
Instead of object detection, we want to try out a more holistic approach to scene understanding: semantic segmentation. The goal of this project is to develop a deep learning model for semantic segmentation that can run in real-time on the embedded CPU of the NAO V6.
- Synthetic Image Generation
In recent years, deep learning models for computer vision have become more ubiquitous in the SPL. However, collecting and annotating training datasets is a very time-consuming task. We want to leverage state-of-the-art photorealistic simulators to generate synthetic datasets.