Student Projects

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See the attached pdf

See the attached pdf

If you are interested, please contact Manish Prajapat, Amon Lahr and Johannes Köhler {manishp, amlahr, jkoehle} @ethz.ch.

In this semester thesis, our goal is to enable an F1Tenth car, an autonomous vehicle at 1:10 scale of a Formula 1 car, to race safely on a track that is perceived through RGB-D images captured by an onboard camera. The lane detection system must be lightweight to ensure real-time performance, as it provides a reference for the controller. Furthermore, the controller must ensure the car's safety on the track while effectively handling potential challenges caused by perception inaccuracies. The performance of the proposed method will be validated through hardware testing.

Our objective is to enable the F1Tenth car to autonomously race on an unknown racing track by perceiving the lane markings through an onboard camera. This thesis focuses primarily on the development of the control module that can be combined with the existing perception stack.

We aim to achieve this objective through the following tasks:

• Setting up the existing perception stack for centerline detection and potentially fine tuning it for the specific experiment setup
• Implementing the method that combines several consecutive centerline estimates into a road map
• Implementing a controller that uses the road map as a reference
• Testing the performance of the method on F1Tenth car

Qualifications:

• Experience with Python
• Experience with implementing model predictive control methods
• Basic knowledge of computer vision and image processing
• Interest in robotics and hardware applications

To apply for this semester thesis, please send your resume and transcript to Jelena Trisovic (tjelena@ethz.ch).

Dr. Johannes Köhler, jkoehle@ethz.ch

marischm@ethz.ch
ffluerenb@ethz.ch

Soft robots offer notable advantages over rigid robots in terms of flexibility and compliance, which facilitate safe and robust interactions with environments. These characteristics make them ideal for diverse applications, such as medical contexts and bionic robotics. However, due to the absence of precise models and the time-varying nature of their system dynamics, achieving optimal control performance on soft robots remains a significant challenge. With the rapid development of machine learning, we now have various learning-based methods to achieve precise pose control of soft robots [4]. Nevertheless, in practice, we have found that the (off-line) well-trained control policies often become ineffective upon deployment due to time-varying system dynamics, which lead us to rethink the traditional “sampling-training-deployment” paradigm in machine learning.

The rise of online learning can effectively enable models to adapt to changing system dynamics. Recent studies have shown that in online learning, even when using an approximate (or even linear) model, it is possible to ensure a sublinear convergence of the regret [2]. Additionally, online learning demonstrates high learning efficiency and stability in both simulations and complex real-world systems.

In this project, we will adopt gradient-based stochastic online learning to achieve precise pose control of a soft manipulator [1,3]. The cable-driven soft robot arm comprises three independent modules (about 0.2m for each module), and each module is actuated independently with three cables. More details of the robot setup, such as structural and mechanical description, can be found in our previous work [3]. We will first establish an approximate model of the system, then learn both feedforward and feedback controllers in an online manner, which enable the soft arm to perform a series of acrobatic demonstrations. Finally, we will compare our approach with some existing offline control algorithms to evaluate the performance in terms of learning efficiency, handling distribution shifts, and tracking accuracy.

Reference

[1] Chen, Z., Guan, Q., Hughes, J., Menciassi, A. and Stefanini, C., 2024. S2C2A: A Flexible Task Space Planning and Control Strategy for Modular Soft Robot Arms. arXiv preprint arXiv:2410.03483.

[2] Ma, H., Zeilinger, M. and Muehlebach, M., 2024. Stochastic Online Optimization for Cyber-Physical and Robotic Systems. arXiv preprint arXiv:2404.05318.

[3] Guan, Q., Stella, F., Della Santina, C., Leng, J. and Hughes, J., 2023. Trimmed Helicoids: An Architectured Soft Structure yielding Soft Robots with High Precision, Large Workspace, and Compliant Interactions. npj Robotics, 1(1), p. 4.

[4] Bern, J.M., Schnider, Y., Banzet, P., Kumar, N. and Coros, S., 2020. Soft Robot Control with A Learned Differentiable Model. International Conference on Soft Robotics, pp. 417-423.

Work packages

Stage 1 - Preparation

• Literature review on state-of-the-art work learning-based methods for soft robotics
• Identify a rough (differentiable) model for the soft manipulator, e.g. system identification in the frequency domain, data-driven method
• Setup the motion capture system for tracking the posture of the soft manipulator

Stage 2 - Combine online learning & soft arm

• Implement iterative learning control (ILC) for the soft manipulator to track a single fixed given reference trajectory
• Implement stochastic online learning method for tracking any reference trajectories (in the workspace of the soft manipulator)

Stage 3 - Analysis & demonstrations

• Quantify the control performance, and compare to a baseline (e.g. an offline trained controller)
• Perform some acrobatics using the soft manipulator for demonstration

Qinghua Guan, qinghua.guan@epfl.ch, EPFL

Hao Ma, haomah@ethz.ch, ETHZ

Cheng Pan, cheng.pan@epfl.ch, EPFL

To apply, please include a short motivation for this project, as well as a copy of your CV and transcripts.

Requirements: experience with a Python deep learning framework, understanding of 3D scene and camera geometry.

Please send us a CV and transcript.

Dr. Hermann Blum (blumh@ethz.ch) Dr. Zuria Bauer (zbauer@ethz.ch) Tifanny Portela (tportela@ethz.ch) Jelena Trisovic (tjelena@ethz.ch)

This project is set to limited visibility by its publisher. To see the project description you need to log in at SiROP. Please follow these instructions:

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See attachment

Dr. Anna Scampicchio, ascampicc@ethz.ch

see attachment

see attachment

Dr. Anna Scampicchio, ascampicc@ethz.ch