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Adversarial Self-Supervised Learning with Digital-Twins (Wintersemester 2021/2022)

Lecturer: Prof. Dr. Holger Giese (Systemanalyse und Modellierung) , Christian Medeiros Adriano (Systemanalyse und Modellierung) , He Xu (Systemanalyse und Modellierung)

General Information

  • Weekly Hours: 4
  • Credits: 6
  • Graded: yes
  • Enrolment Deadline: 01.10.2021 - 22.10.2021
  • Teaching Form: Project seminar
  • Enrolment Type: Compulsory Elective Module
  • Course Language: English

Programs & Modules

Data Engineering MA
Digital Health MA
  • APAD-Concepts and Methods
  • APAD-Technologies and Tools
  • APAD-Specialization
IT-Systems Engineering MA

Description

The Tesla engineers manually specified 221 triggers that are activated in their fleet [1]. The  models run in a "shadow mode", which works as a digital twin [2-6] of the real system that allows to deal with disagreements between the camera and Lidar (e.g., bounding box jitter or distinct decisions from user and the model predictive control unit), which sent to the Tesla engineers. The engineers then "manually" analyze the data, apply an auto labelling mechanism and add this data to the training set. In other words, it is almost a closed AI loop, except for the engineering analysis. This corroborates the need for principled engineering methods. Even when these methods are explicit, like in Tesla example, they are evident in the involvement of the engineers to determine the prior-knowledge and analyze its consequence in ways (auto-labelling model) that informs further design decisions (inclusion of features to the training set).

Prediction models for decision-making can be efficiently (and safely) learned from data that is realistic enough. However, this would involve letting machine learning models learn in the production environment, which is costly and risky. One alternative is to generate environments that are as similar as possible as the production. This is how digital-twins play a role. That opens opportunities and further interesting challenges, like dealing with the differences between the simulated and real world environments. Among many alternatives, we will explore methods for domain randomization [7], generalization, and transfer-learning which work as adversarial environments [8].

Literature

  1. Karpathy, A., 2021, - Closed AI Loop at Tesla for Labeling data that Trigger Corner Cases, Keynote at CVPR’21
  2. Ahlgren, J., et al., 2021, Facebook’s Cyber–Cyber and Cyber–Physical Digital-Twins
  3. Xia, K., et al., 2021, A digital twin to train deep reinforcement learning agent for smart manufacturing plants: Environment, interfaces and intelligence. Journal of Manufacturing Systems, 58, 210-230.
  4. Rathore, M. M., et al., 2021, The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities. IEEE Access, 9, 32030-32052.
  5. Suhail, S., et al., 2021, Blockchain-based Digital Twins: Research Trends, Issues, and Future Challenges
  6. Creating “Digital Twins” at scale, https://news.mit.edu/2021/creating-digital-twins-scale-0614 
  7. Peng, X. B., et al., 2018, Sim-to-real transfer of robotic control with dynamics randomization, IEEE ICRA
  8. Jiang, Y., 2021, Learning an Accurate Physics Simulator via Adversarial Reinforcement Learning 

Learning

The course is a project seminar, which has an introductory phase comprising initial short lectures. After that, the students will work in groups on jointly identified experiments applying specific solutions to given problems and finally prepare a presentation and write a report about their findings concerning the experiments.

There will be an introductory phase to present basic concepts for the theme, including the necessary foundations.

Examination

We will grade the group's experiments (60%), reports (30%), and presentations (10%). Participation in the project seminar during meetings and other groups' presentations in the form of questions and feedback will also be required.

Dates

Dates will be announced soon. If you are interested in this course, please contact christian.adriano(at)hpi.de .

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