Project Database

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Machine learning has shown great potential for enabling robots to acquire robust and adaptive locomotion skills. For hyper-redundant robots, such as salamander-inspired robots, this remains challenging because of the high-dimensional body dynamics, the diversity of possible behaviors, and the need to integrate multimodal sensory feedback from both terrestrial and aquatic environments.

This project is an extension of a previous project, in which we will keep exploring learning-based methods for improving salamander robot locomotion in complex amphibious environments. Possible directions include sensor fusion, maneuvering, transition, sim-real transfer, etc. A key goal will be to evaluate whether biological/physical priors can improve learning efficiency, robustness, and smooth transitions between behaviors.

The project is suitable for a highly self-motivated student with complete project experience in machine learning. Familiarity with MuJoCo MJX or other physics simulators is expected. Experience with CPGs, signal processing, reinforcement learning, or sensor-based control will be helpful.

Students who are interested in this project shall send the following materials to the assistant: (1) resume, (2) transcript showing relevant courses and grades, and (3) other materials that can demonstrate their skills and project experience (such as videos, slides, Git repositories, etc.).

This project is part of an ongoing collaboration with Prof. Eva Naumann at Duke University (USA) to study and model zebrafish sensorimotor integration using simulation and robots. It follows two collaborative studies that were published in Science robotics, see Artificial embodied circuits uncover neural architectures of vertebrate visuomotor behaviors, Energy efficiency and neural control of continuous versus intermittent swimming in a fishlike robot and FARMS: Framework for Animal and Robot Modeling and Simulation.

Depending on the interest of the student, there are several new avenues to be explored. These include: (i) improving the neuromechanical simulation and integrating it into a new simulation framework (FARMS, link), (ii) applying deep reinforcement learning for designing biologically constrained sensory-motor circuits, (iii) investigating new visually-guided behaviors, and/or (iv) programming and improving ZBot for neuroscience related studies.

The project will normally take place at EPFL, but could also happen at Duke University if there is interest.

This project has been taken.

Amphibious robots must move across environments where fluid forces, body contact, and solid structures interact in complex and often unpredictable ways. These interactions are difficult to model accurately, making adaptive and sample-efficient control especially important.

In this project, we will explore how online optimization can improve CPG-based amphibious locomotion controllers. Using onboard sensors such as contact, flow, and proprioceptive sensing, the robot will tune its controller to achieve more agile, efficient, and robust multimodal locomotion. Depending on the project scope, the work may focus on sample-efficient optimization in simulation and simulation-to-robot transfer.

This project is suitable for students who have experience in optimization and MuJoCo simulations. Experience in CPG networks, system identification, signal processing, Arduino/Raspberry Pi programming, and ROS 2 can be very helpful.

Students who are interested in this project shall send the following materials to the assistant: (1) resume, (2) transcript showing relevant courses and grades, and (3) other materials that can demonstrate their skills and project experience (such as videos, slides, Git repositories, etc.).

In nature, animals have many compliant structures that benefit their locomotion. For example, compliant foot/leg structures help adapt to uneven terrain or negotiate obstacles, flexible tails allow efficient undulatory swimming, and muscle-tendon structures help absorb shock and reduce energy loss. Similar compliant structures may benefit salamander-inspired robots as well.

In this study, the student will try simulating compliant structures (the feet of the robot) in Mujoco and optimizing the design. To bridge the sim-to-real gap, the student will first work with other lab members to perform experiments to measure the mechanical properties of a few simple compliant structures. Then, the student needs to simulate these experiments using the flexcomp plugin of Mujoco or theoretical solid mechanics models, and tune the simulation models to match the dynamical response in simulation with the experiments. Afterward, the student needs to optimize the design parameters of the compliant structures in simulation to improve the locomotion performance of the robot while maintaining a small sim-to-real gap. Finally, prototypes of the optimal design will be tested on the physical robot to verify the results.

The student is thus required to be familiar with Python programming, physics engines (preferably Mujoco), and optimization/learning algorithms. The student should also have basic mechanical design abilities to design mechanical structures and perform experiments. Students who have taken the Computational Motor Control course or have experience with data-driven design and solid mechanics would also be preferred.

The student who is interested in this project shall send the following materials to the assistants: (1) resume, (2) transcript showing relevant courses and grades, and (3) other materials that can demonstrate your skills and project experience (such as videos, slides, Git repositories, etc.).

Why do horses and and camels both walk at slow speeds and gallop at fast speeds, but at intermediate speeds horses prefer to trot while camels pace? While gait transitions have been well studied for a given morphology, these models rarely explain when and why animals prefer different or gaits despite being quite similar, or the same gaits despite having very different morphologies. This project tackles this question through the lens of reinforcement learning (RL), with a focus on the role of entrainment between an internal oscillator model and the mechanical dynamics, i.e the morphology. You will explore both top-down and bottom-up coupling mechanisms, unconventional reward functions such as viability measures, and benchmark these approaches across different morphological parameters (e.g length-to-height and width-to-height ratios, mass). Stretch goals can include evaluating the role of active exploration in a hierarchical RL setup, exploring sprawling or bipedal morphologies, changing morphology during learning (e.g. growth), or you may propose something in discussion with the supervisors.

NOTE: this is a collaboration project, to be conducted at Cornell University, USA. To apply, e-mail Steve Heim stating why you are interested in this project (brief, 1-2 sentences each), and attach your CV and transcript.

This project has been taken

Animals display a rich diversity of behaviors in natural environments, but only a small set of them have been well studied and simplified into template gaits. Recent advances in imitation learning have enabled quadruped and humanoid robots to reproduce complex animal and human motions, but these approaches have been less explored for amphibious robots that must coordinate body, limbs, and environmental interactions across water and land.

In this project, we will work on the recordings of real salamanders in a vivarium with various terrains. We will extract multi-terrain locomotion behaviors from the collected motion data. Then we will investigate how diverse salamander behaviors can be transferred to simplified salamander models or salamander-inspired robots. Note that because we rely on DeepLabCut to track the animal kinematics from videos, the student may spend half a semester on labeling video data , unless a better tracking method is found or developed.

The student is expected to be familiar with Python programming, signal processing, kinematics, and imitation learning. Experience with dimensionality reduction, MuJoCo simulation, and CPGs is highly recommended.

Students who are interested in this project shall send the following materials to the assistants: (1) resume, (2) transcript showing relevant courses and grades, and (3) other materials that can demonstrate their skills and project experience (such as videos, slides, Git repositories, etc.).

This master thesis will use paleorobotics to test whether extinct armored mammals could physically generate powerful tail-club strikes. The successful candidate will build MuJoCo and SIMM/OpenSim-type models of a segmented, tendon-driven tail, quantify how joint compliance and soft tissues reshape impact forces, and create an energy dissipation map showing how strike energy is transferred, absorbed, or transmitted through the body. Optional work includes designing a biomimetic hexagonal carapace and a tendon-driven robotic tail prototype. This project will take place both at EPFL and University of Zurich (approx. 60%/40%, TBD). Please submit your resume/CV along with a cover letter detailing your relevant experience and why you are excited about this exceptional thesis opportunity to Auke Ijspeert as well as Ardian Jusufi.

The goal of this project is to develop a bidirectional serial link using a CC1120 radio chip on 169.4 MHz. A prototype currently exists, but has several issues. The project should finalize the prototype to a final product, which includes:

• Redesigning the printed circuit boards so they will be usable as-is by the final users
• Improving the communication software to remove existing issues (which mainly happen at low speed) and improve stability
• Implementing a very simple TUI on the microcontroller to allow users to select a remote device and test communication
• Adding flow control on the robot side

Requirements for the project are:

• Previous experience with electronic design, ideally PCB design
• Microcontroller programming in C
• Basics of communication (UARTs, flow control, protocols)

We offer an exciting opportunity for a highly motivated graduate student in Mechanical Engineering to undertake a thesis project focusing on designing and constructing a robotic apparatus to test and validate the impact force of a dinosaur tail strike. This project spans approximately 6 months and requires a combination of mechanical design expertise, force plate measurements, innovation in biomimetic structures, and proficiency in data analysis.

Project Description

The thesis project revolves around designing, building, and controlling a life-sized robotic tail capable of replicating the striking force of a dinosaur’s club-shaped tail. The aim is to accurately measure impact force and velocity using a bone-like material reproduction sourced from fossils we have at the Palaeontological Institute and Museum of the University of Zurich. This endeavor will involve close collaboration with a multidisciplinary team and conducting experiments at our facilities at Empa Dübendorf by Zurich.

Responsibilities

• Utilize mechanical design skills (3D modeling) and motion control (microcontroller designing and programming) to create a functional life-sized Glyptodont's tail.
• Conduct tests to measure impact force and velocity, meticulously documenting experimental procedures and results.
• Employ data analysis techniques, including statistical tools or software, to interpret experimental findings.
• Demonstrate creativity in problem-solving, proposing enhancements to the biomimetic tail design where necessary.
• Collaborate effectively within a team, communicating ideas and contributing to the project's success.

Requirements

Background in mechanical designing with proficiency in 3D modeling.

Expertise in motion control, including microcontroller designing and programming.

Ability to collect, analyze, and interpret experimental data using statistical tools or software.

Strong problem-solving skills with a demonstrated ability to innovate in design and testing.

Excellent communication skills to collaborate within a team and articulate ideas effectively.

Expected Outcomes

Successful creation of a fully functional life-sized Glyptodont's tail within the thesis duration.

Execution of tests to accurately measure impact force and velocity.

Comprehensive documentation of experiments and results.

Recommendations for potential enhancements or modifications based on findings.

If you are a Master's student passionate about pushing the boundaries of robotics, biomimicry, and mechanical engineering and are looking for an engaging thesis project, we encourage you to apply. Please submit your resume/CV along with a cover letter detailing your relevant experience and why you are excited about this exceptional thesis opportunity to Auke Ijspeert as well as Ardian Jusufi.