Biorobotics Laboratory BioRob

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Project Database

This page contains the database of possible research projects for master and bachelor students in the Biorobotics Laboratory (BioRob). Visiting students are also welcome to join BioRob, but it should be noted that no funding is offered for those projects. To enroll for a project, please directly contact one of the assistants (directly in his/her office, by phone or by mail). Spontaneous propositions for projects are also welcome, if they are related to the research topics of BioRob, see the BioRob Research pages and the results of previous student projects.

Search filter: only projects matching the keyword Computer Science are shown here. Remove filter

Amphibious robotics
Computational Neuroscience
Dynamical systems
Human-exoskeleton dynamics and control
Humanoid robotics
Miscellaneous
Mobile robotics
Modular robotics
Neuro-muscular modelling
Quadruped robotics


Quadruped robotics

A small excerpt of possible projects is listed here. Highly interested students may also propose projects, or continue an existing topic.

743 – Quadruped Robot Projects (Several)
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Category:semester project, master project (full-time)
Keywords:Agility, Artificial muscles, Bio-inspiration, C++, Computer Science, Control, Experiments, Learning, Locomotion, Machine learning, Muscle modeling, Online Optimization, Optimization, Programming, Python, Quadruped Locomotion, Robotics, Simulator, Vision
Type:10% theory, 20% hardware, 70% software
Responsible: (MED 1 1024, phone: 37506)
Description:There are several quadruped robot projects available related to locomotion, jumping, and human-robot interaction, with methodologies including deep reinforcement learning, imitation learning, optimal control, and computer vision. Students who already have experience with deep learning, C++, vision, and who have worked with hardware are especially encouraged to apply. Please send Guillaume your CV, transcript, and explain your motivation on what kind of topics you would be interested in working on (more details = better!).

Last edited: 18/07/2024

Miscellaneous

742 – Create synthetic salamander dataset with domain randomization and unsupervised generative attentional networks
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Category:semester project, master project (full-time)
Keywords:3D, C++, Computer Science, Data Processing, Machine learning, Programming, Vision
Type:20% theory, 80% software
Responsible: (MED 1 1611, phone: 36620)
Description:

Powerful deep-learning based tracking method for animal behaviors requires large-scale curated and annotated data. Several recent papers [1,2] revealed the possibility to leverage the data requirement by rendering animated synthetic animals such as mice and ants.

In this project, the student will work on an existing biomechanical model of Salamander Pleurodeles Waltl to create a synthetic dataset for marklerless keypoint tracking tasks. The dataset would help improve the performance of a salamander tracking network, which would ultimately provide invaluable kinematics data for designing muscle models, neural controllers and validating neuroscience hypothesis.

For a PdM, this project involves:

  • Improve the realism of the current salamander model in Blender and add diversity with procedurally generated noise and domain randomization.
  • Generate a synthetic image dataset for markerless tracking tasks.
  • Train an Image Domain Translator (e.g. U-GAT-IT[3]) to increase the dataset fidelity and reduce the reality gap
  • Bonus: evaluate the dataset power on a markerless tracking network (e.g. DLC[4])

For a semester project, work packages will be optionally dropped and tailored according to student’s skills and interests.

The student is expected to have good programming skills and previous experience/knowledge in deep learning. Knowledge in 3D modeling and computer graphics is a plus but not required. If interested, please send an email to Chuanfang Ning with your motivation, CV, transcripts and most relevant experience.

[1] Bolaños, Luis A., et al. "A three-dimensional virtual mouse generates synthetic training data for behavioral analysis."

[2] Plum, Fabian, et al. "replicAnt: a pipeline for generating annotated images of animals in complex environments using Unreal Engine." 

[3] Kim, Junho, et al. "U-gat-it: Unsupervised generative attentional networks with adaptive layer-instance normalization for image-to-image translation." 

[4] Mathis, Alexander, et al. "DeepLabCut: markerless pose estimation of user-defined body parts with deep learning." 



Last edited: 27/06/2024

2 projects found.