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.
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This project aims to continue the development of a series elastic actuator (SEA) for a salamander robot. Despite the availability of various off-the-shelf servo motors, it is difficult to find one that can provide accurate torque/force control to validate advanced control methods involving musculoskeletal models, provide large torque output, and be compact in size. SEAs are promising in satisfying these requirements, see this paper as an example: http://biorobotics.ri.cmu.edu/papers/paperUploads/DSCC2013-3875.pdf A preliminary design of the geared motor that will drive the elastic component has been completed. This project will mainly focus on the design and manufacturing of the elastic component, the programming of the electronics, and the design of the feedback controller. Multiple iterations of testing and improvement will be needed, so the student is expected to have great time management skills. If there is sufficient time, the following topics can be explored: (1) Modify the design to test hypotheses about multiarticular muscles. (2) Integrate the motor to a salamander robot and test various scientific hypotheses. (3) Waterproofing the motor module for amphibious applications. Students with a solid background in mechanical design and control theory are preferred. Interested students could send CVs, transcripts, materials that can demonstrate project experience (videos, slides, reports, etc.), if possible, and several potential time slots for a quick meeting to qiyuan.fu@epfl.ch.
In this project, the student is expected to continue developing the existing firmware for high-performance low-level control of the new Pleurobot (our amphibious legged robot modeling Pleurodeles waltl) and its multiple sensors. The major objectives include: (1) Improve the sampling speed and robustness of the microcontrollers that collect data from multiple sensors. (2) Increase the bandwidth of and reduce the latency in the communication between the onboard computer and multiple microcontrollers. (3) (For full-time students) Develop low-latency wireless communication between the onboard computer and the user's laptop for remote control. The student is expected to be familiar with (1) communication protocols including SPI, UART, and CAN, and (2) programming of embedded systems using C/C++. Knowledge about signal processing, wireless network protocols, and/or GUI development can be a bonus. The student who is interested in this project could send his/her transcript, CV, and description of their past project experience to qiyuan.fu@epfl.ch. A student who can work full-time in the summer or the autumn semester is preferred.
Exoskeletons have experienced an unprecedented growth in recent years and hip-targeting active devices have demonstrated their potential in assisting walking activities. Portable exoskeletons are designed to provide assistive torques while taking off the added weight, with the overall goal of increasing the endurance, reducing the energetic expenditure and increase the performance during walking. The design of exoskeletons involves the development of the sensing, the actuation, the control, and the human-robot interface. In our lab, a hip-joint active hip orthosis (“eWalk”) has been prototyped and tested in recent years. Currently, multiple projects are available to address open research questions. Does the exoskeleton reduce the effort while walking? How can we model human-exoskeleton interaction? How can we design effective controls? How can we optimize the interfaces and the control? Which movements can we assist with exoskeletons? To address these challenges, the field necessitates knowledge in biology, mechanics, electronics, physiology, informatics (programming, learning algorithms), and human-robot interaction. If you are interested in collaborating in one of these topics, please send an email to giulia.ramella@epfl.ch with your CV, previous experiences that could be relevant to the project, and what interests you the most about this research topic (to be discussed during the interview).
Last edited: 19/04/2024
Quadruped robotics
A small excerpt of possible projects is listed here. Highly interested students may also propose projects, or continue an existing topic.
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!).
Recent years have shown impressive locomotion control of dynamic systems through a variety of methods, for example with optimal control (MPC), machine learning (deep reinforcement learning), and bio-inspired approaches (CPGs). Given a system for which two or more of these methods exist: how should we choose which to use at run time? Should this depend on environmental factors, i.e. the expected value of a given state? Can this help with explainability of what exactly our deep reinforcement learning policy has learned? In this project, the student will use machine learning to answer these questions, as well as integrate CPGs and MPC into the deep reinforcement learning framework. The methods will be validated on systems including quadrupeds and model cars first in simulation, with the goal of transferring the method to hardware. To apply, please email Guillaume with your motivation, CV, and briefly describe your relevant experience (i.e. with machine learning, software engineering, etc.).
When recording animal behaviors using optical or X-ray videos, there is a tradeoff between having a large field of view and having a high resolution of the animal body. This limits the ability to obtain animal kinematics for a long time and with high accuracy simultaneously. One solution is to let the animal run on a treadmill such that it can stay inside the field of view. However, the animal often varies its speed during movement, and the radio-opaque components in the common treadmills add difficulty in placing X-ray cameras. In this project, the student will develop a treadmill to be used with optical and X-ray tracking setup. The treadmill is expected to have the following features: (1) The major components should be constructed using radio-transparent materials such as plastics. (2) The slope of the treadmill can be adjusted. (3) The speed of the treadmill can be controlled in closed loops to keep the animal in the center of the view. To realize this, a camera may be used to track the animals. See this video for an example: https://www.youtube.com/watch?v=0GyovqfQj2g&ab_channel=TerradynamicsLab (Note that the treadmill in this project does not need to move in 2 dimensions.) If there is sufficient time, the following features would be desirable: (4) Being able to move in two dimensions (omnidirectional treadmill). (5) Allowing integration with force/torque sensors below the surface. Students with knowledge of designing mechanical structures and embedded systems, computer vision, and feedback control are preferred. Interested students can send their resumes, transcripts, and materials that can show their project experience to the assistants.
Controlling vehicles at their limits of handling has significant implications from both safety and autonomous racing perspectives. For example, in icy conditions, skidding may occur unintentionally, making it desirable to safely control the vehicle back to its nominal working conditions. From a racing perspective, drivers of rally cars drift around turns while maintaining high speeds on loose gravel or dirt tracks. In this project, the student will compare several approaches for high speed, dynamic vehicle maneuvers, including NMPC with a standard dynamic bicycle model, NMPC with a dynamic bicycle model + GP residuals, NMPC with learned dynamics (i.e. a NN), and lastly a pure model-free reinforcement learning approach. All approaches will be tested in both simulation as well as on a scaled vehicle hardware platform. To apply, please email Guillaume with your motivation, CV, and briefly describe your relevant experience (i.e. with machine learning, software engineering, etc.).
