Syllabus
Introduction
Cognitive robotics and social robotics are related, but distinct fields within robotics. Cognitive robotics is focused on creating robots that can perform tasks that require advanced perception, reasoning, and decision-making abilities. These tasks may include recognizing objects, understanding natural language, learning from experience, and making predictions about the future. Cognitive robots are designed to be able to process and analyze large amounts of data, and to make decisions based on that data. Social robotics, on the other hand, is focused on creating robots that can interact and communicate with humans in a natural and effective way. Social robots are designed to be able to understand and respond to human emotions, social cues, and body language. They are also designed to be able to generate expressive and natural-sounding speech and gestures. Social robots are intended to be used in a variety of applications, such as customer service, education, and healthcare. Both cognitive and social robotics requires the use of advanced technologies such as machine learning, computer vision, and natural language processing. However, cognitive robotics is more focused on the robot’s ability to process information and make decisions, while social robotics is more focused on the robot’s ability to interact with humans.
Cognitive and social robotics enables the development of robots that can work in collaboration with human workers, improving the overall safety and performance of the workplace. Moreover, cognitive and social robotics enables the creation of robots that are able to interact with their environment and make decisions based on their perceptions, providing the opportunity for greater flexibility and autonomy in the production process. Cognitive and social robotics is becoming increasingly important in industrial production for several reasons. The ability to use robots in a way that leverages their cognitive and social capabilities can have a profound impact on industrial production. By enabling robots to work in a more collaborative and adaptive manner, it becomes possible to reduce the time and effort required to perform tasks, improve product quality and reliability, and minimize the impact of machine downtime and failure. For example, in a manufacturing setting, robots equipped with cognitive abilities can monitor production processes in real-time, identify potential problems, and make decisions to address those problems before they lead to equipment failures or production slowdowns. Additionally, social robots can interact with human workers in a production environment, providing assistance with tasks, monitoring worker safety, and collecting data that can be used to improve production processes.
Total Hours
This course unit covers 100 hours, from which 28 hours lectures, 14 hours lab work, and 58 hours individual study and work.
General Objective
The general objective of a course in cognitive and social robotics for industrial production is to equip students with the knowledge and skills necessary to design, develop, and integrate socially intelligent robots into various industrial settings. The course covers the principles of cognitive and social robotics, including human-robot interaction, machine learning, computer vision, and natural language processing. Students will learn how to design socially intelligent robots that can interact with human operators in a natural and intuitive way, and how to use these robots to enhance the efficiency, safety, and overall quality of industrial production processes. Through hands-on projects and case studies, students will gain practical experience in developing socially intelligent robots and integrating them into real-world industrial environments.
Specific Objectives / Learning Outcomes
The specific objectives or learning outcomes of this course unit are:
- Understanding the concepts and principles of cognitive and social robotics, and their applications in industrial production.
- Acquiring knowledge of the various sensing, perception, and decision-making capabilities of cognitive and social robots, and their impact on industrial production.
- Developing skills in programming, controlling, and integrating cognitive and social robots into industrial production processes.
- Familiarizing with the latest technologies and trends in cognitive and social robotics for industrial production, including IoT, cloud computing, and machine learning.
- Applying the knowledge and skills gained from the course to real-world industrial production problems, through hands-on laboratory work, projects, and case studies.
- Evaluating the potential benefits and challenges of integrating cognitive and social robots into industrial production processes, and proposing solutions to overcome these challenges.
- Developing a critical perspective on the ethical, social, and economic implications of cognitive and social robotics for industrial production.
Professional Competencies
The professional competencies that a student can gain from this course unit include:
- Knowledge and understanding of the basics of cognitive and social robotics and its applications in industrial production.
- Knowledge and understanding of the fundamental principles of human-robot interaction, including the design and implementation of interfaces, algorithms, and software for social robots.
- Knowledge and understanding of the ethical, legal, and societal implications of deploying social robots in industrial production, including data privacy, security, and other important considerations.
- Ability to develop, implement, and evaluate systems for integrating social robots into industrial production, including design and development of software and algorithms.
- Ability to work effectively with other professionals and stakeholders, including industrial engineers, technicians, and production managers, to design, develop, and integrate social robots into industrial production.
- Understanding of how to use data and analytics to optimize the deployment and performance of social robots in industrial production, including how to develop and implement predictive maintenance strategies.
- Ability to evaluate and interpret the results of experiments and case studies to inform decision-making and improve the performance of social robots in industrial production.
- Ability to communicate effectively with a wide range of stakeholders, including engineers, technicians, production managers, and customers, to explain the benefits and limitations of social robots in industrial production and to promote their effective integration into the workplace.
Cross Competencies
The course unit on Cognitive and Social Robotics for industrial production develops the following cross-competencies in students:
- Problem-Solving: Students will develop their problem-solving skills as they learn how to design, program, and integrate social robots into industrial production systems.
- Innovation: Students will learn how to identify new opportunities for using social robots in industrial production, and how to develop innovative solutions to improve production processes.
- Teamwork: Students will learn how to work in teams, collaborating with engineers and technicians from different disciplines to design and implement social robotics solutions for industrial production.
- Communication: Students will develop their communication skills as they work with stakeholders to explain the benefits and limitations of social robots, and how they can be used in industrial production.
- Adaptability: Students will learn how to work in an ever-changing environment, adapting to new technologies and changing business requirements as the use of social robots in industrial production evolves.
- Cross-cultural understanding: Students will gain an understanding of the cultural and ethical implications of using social robots in industrial production, and will learn how to work with stakeholders from diverse backgrounds.
- Ethical awareness: Students will develop their ethical awareness as they learn about the responsible use of social robots in industrial production, including data privacy and security, human-robot interaction, and ethical decision-making.
Alignment to Social and Economic Expectations
Evaluation
Assessment methods
For the lectures portion of the course unit on cognitive and social robotics in industrial production, the following assessment methods are used:
- Quizzes: In-class quizzes or online quizzes to test the students’ understanding of key concepts and theories covered in the lectures.
- Written assignments: Individual assignments that require students to apply their knowledge and skills to solve a real-world problem or case study.
- Midterm and Final Exams: These exams consist of multiple choice, short answer, and essay questions and assess the students’ overall understanding of the course material.
For the lab work portion of the course, the following assessment methods are used:
- Lab reports: Students are required to write lab reports documenting their experiments, results, and analysis. These reports are graded on the quality of their writing, methodology, results, and analysis.
- Oral presentations: Students are required to present their lab work to the class, which is assessed based on the quality of their presentation skills, content, and interaction with the audience.
Assessment criteria
For lectures, the assessment criteria for this course unit on ognitive and social robotics in industrial production are:
- Knowledge and Understanding: Assessment of the student’s ability to comprehend and apply the concepts, theories, and principles of ognitive and social robotics in industrial production.
- Analytical and Problem Solving Skills: Assessment of the student’s ability to analyze complex problems, evaluate different solutions, and make informed decisions related to ognitive and social robotics in industrial production.
- Communication Skills: Assessment of the student’s ability to communicate their ideas, designs, and solutions in a clear, concise, and effective manner.
- Teamwork and Collaboration Skills: Assessment of the student’s ability to work effectively in a team and collaborate with others to achieve a common goal.
- Application of Technology: Assessment of the student’s ability to apply appropriate technologies, tools, and software of cognitive and social robotics in industrial production.
For lab work, the assessment criteria could include:
- Technical Skills: Assessment of the student’s ability to use and apply the technical skills and knowledge acquired in the course to cognitive and social robotics solutions in industrial production.
- Quality of Work: Assessment of the student’s ability to produce high-quality work that meets the requirements and standards set for cognitive and social robotics in industrial production.
- Creativity and Innovation: Assessment of the student’s ability to think creatively and apply innovative solutions of cognitive and social robotics in industrial production.
- Attention to Detail: Assessment of the student’s ability to pay close attention to details and ensure that cognitive and social robotics solutions are accurate, complete, and well-documented.
- Time Management: Assessment of the student’s ability to manage their time effectively and deliver completed lab work within the specified timeframe.
Quantitative performance indicators to assess the minimum level of performance (mark 5 on a scale from 1 to 10)
Quantitative performance indicators to assess the minimum level of performance (mark 5 on a scale from 1 to 10) for the lectures:
- Attendance and participation in class discussions – The student should attend at least 80% of the lectures and actively participate in class discussions.
- Homework and Quizzes – The student should complete all homework assignments and quizzes with a minimum score of 60%.
- Midterm Exam – The student should achieve a minimum score of 50% on the midterm exam.
Quantitative performance indicators to assess the minimum level of performance (mark 5 on a scale from 1 to 10) for the lab works:
- Lab attendance and participation – The student should attend and participate in all scheduled lab sessions.
- Lab reports – The student should submit all lab reports on time, with a minimum score of 60% on each report.
- Lab assignments – The student should complete all lab assignments with a minimum score of 60%.
- Lab exams – The student should achieve a minimum score of 50% on the lab exams.
Quantitative performance indicators for the final exam to assess the minimum level of performance:
- Completion of a minimum number of lecture-related questions correctly – 70% of the total questions.
- The student should be able to demonstrate an understanding of the basic concepts and theories related to cognitive and social robotics and their applications in industrial production, with a minimum score of 50% on multiple-choice questions or short answer questions.
- The student should be able to explain and analyze real-life case studies and their results, with a minimum score of 50% on case study analysis questions.
- The student should be able to demonstrate a basic knowledge of the technologies, tools, and methodologies used in the design and implementation of cognitive and social robotics, with a minimum score of 50% on matching or labeling questions.
- The student should be able to apply the concepts and theories learned in the lectures to solve practical problems, with a minimum score of 50% on problem-solving questions.
- The student should be able to critically evaluate the benefits and challenges of using cognitive and social robotics in industrial production, with a minimum score of 50% on essay questions.
- Evidence of the ability to apply learned concepts and theories to practical scenarios, as demonstrated by the number of correctly answered application-based questions.
- Display of critical thinking skills, as evidenced by the number of correct answers to questions requiring analysis and synthesis of information.
- Overall exam performance, measured in terms of the total number of correct answers and expressed as a percentage of the total exam score. A minimum score of 50% or above is set as the benchmark for a mark of 5.
Lectures
Unit 1: Cognitive Robotics (2 hours)
- Definition and principles of cognitive robotics
- Techniques and algorithms used in cognitive robotics (e.g., machine learning, computer vision, natural language processing)
- Applications of cognitive robotics in industry
Unit 2: Social Robotics (2 hours)
- Definition and principles of social robotics
- Techniques used in social robotics (e.g., human-robot interaction, emotional intelligence)
- Applications of social robotics in industry
Unit 3: Human-Robot Interaction (2 hours)
- Human factors in robot design
- Techniques for human-robot interaction (e.g., gesture recognition, speech recognition)
- Ethics and safety in human-robot interaction
Unit 4: Introduction to Nao and Pepper Robots (2 hours)
- Overview of the hardware and software of Nao and Pepper robots
- Basic programming concepts for Nao and Pepper robots
- Applications of Nao and Pepper robots in industry and research
Unit 5: Nao and Pepper Programming (2 hours)
- Basic programming with Nao and Pepper robots
- Choregraphe, Python, Java and QiChat programming for Nao and Pepper robots
- Motion and gesture control with Nao and Pepper robots
Unit 6: Chatbots (2 hours)
- Overview of chatbots and their history
- Techniques used in chatbot development (e.g., rule-based systems, machine learning)
- Applications of chatbots in industry and customer service
Unit 7: Chatbot Development (2 hours)
- Basic programming for chatbots (e.g., RASA and Python)
- Natural language processing for chatbots
- Integration of chatbots with other technologies (e.g., voice assistants, messaging platforms)
Unit 8: Introduction to Furhat Robot (2 hours)
- Overview of the hardware and software of the Furhat robot
- Basic programming concepts for the Furhat robot
- Applications of the Furhat robot in industry and research
Unit 9: Furhat Programming (2 hours)
- Basic programming with the Furhat robot
- Motion and gesture control with the Furhat robot
- Voice and speech recognition with the Furhat robot
Unit 10: Social Robotics in Manufacturing (2 hours)
- Overview of Industry 4.0 and its impact on manufacturing
- Role of social robots in smart factories
- Case studies and applications of social robots in manufacturing
Unit 11: Cognitive Robotics in Maintenance (2 hours)
- Maintenance tasks and their challenges
- Role of cognitive robots in predictive maintenance
- Case studies and applications of cognitive robots in maintenance
Unit 12: Advanced Topics in Cognitive Robotics (2 hours)
- Reinforcement learning and decision-making in cognitive robotics
- Explainable AI and transparency in cognitive robotics
- Integration of cognitive robotics with other technologies (e.g., IoT, AR/VR)
Unit 13: Advanced Topics in Social Robotics (2 hours)
- Emotion recognition and expression in social robotics
- Social and ethical implications of social robotics
- Integration of social robotics with other technologies (e.g., VR, smart homes)
Unit 14: Future Trends and Challenges in Robotics (2 hours)
- Emerging trends and technologies in robotics (e.g., swarm robotics, bio-inspired robotics)
- Challenges and opportunities in robotics research and development
- Implications and ethical considerations for the future of robotics
Lab Work
Unit 1: Architecture of Social Robots (2 hours of class work)
Objective: To introduce students to the architecture, software settings, remote monitoring, and specialized libraries of Nao, Pepper, and Furhat social robots.
- Architecture of Nao and Pepper robots.
- Architecture of Furhat robots.
- Software settings for Nao and Pepper robots.
- Software settings for Furhat robots.
- Remote monitoring.
- Specialized libraries.
Unit 2: ”Programming” Nao and Pepper Robots with Choregraphe Objects (2 hours of class work and 5 hours of individual work)
Objective: To develop a comprehensive understanding of the Choregraphe objects and the programming concepts involved in developing an application using Choregraphe.
- User interface in Choregraphe.
- Object boxes.
- Creating and editing boxes.
- Creating behaviours.
- Application development in Choregraphe.
- Testing various Choregraphe boxes.
Unit 3: Programming Nao Robots with Python in Choregraphe (2 hours of class work and 5 hours of individual work)
Objective: To develop complex applications with Nao and Pepper robots using Python and Choregraphe environment.
- Creating code in Python boxes.
- Creating an app from scratch.
Unit 4: Programming Nao Robots with Python via API (2 hours of class work and 5 hours of individual work)
Objective: To develop complex applications for gesturing, face recognition, TTS/STT, movement, etc.
- Basic client-server aplications.
- Applications with environment perception.
- Creating new functions.
- Creating brokers and modules.
- Qi framework Python API.
Unit 5: Interactive Robot-Human Dialog using QiChat and Python for Nao Robots (2 hours of class work and 5 hours of individual work)
Objective: To create advanced dialogues between the robot and humans using a specialized language.
- QiChat: basic elements (will be studied in advance by students).
- QiChat sintax (will be studied in advance by students).
- QiChat examples.
- Python in QiChat.
- QiChat in Python.
Unit 6: Design CobiotX* Applications with Pepper Robots (2 hours class of work and 5 hours of individual work)
Objective: To create a basic application with Pepper to interact with humans.
- Install Pepper SDK for Android plug-in and Java QiSDK library.
- Creating a robot application with Java or Kotlin.
- Running an application on the Pepper’s tablet.
Note: The Pepper SDK for Android is a software development kit for programming applications for the Pepper robot, which is a humanoid robot designed for human interaction. The SDK allows developers to create custom applications for the robot using the Java programming language and the Android operating system. With the Pepper SDK for Android, developers can access the robot’s various sensors and actuators, such as its cameras, microphones, speakers, and touch sensors, to create applications that allow the robot to interact with its environment and with people. The SDK also provides access to the robot’s natural language processing and speech recognition capabilities, enabling developers to create conversational interfaces for the robot.
Kotlin is a statically typed, cross-platform, general-purpose programming language that was designed to be fully interoperable with Java. Kotlin was designed to address some of the limitations and verbosity of Java, and provides a more concise and expressive syntax for building software. At the same time, it is fully compatible with Java and can be used in the same codebase as Java, making it an easy transition for Java developers. This allows developers to write cleaner, more concise code and take advantage of new language features and enhancements, while still being able to use existing Java code libraries and tools.
Unit 7: Developing Advanced Application with Pepper Robots (2 hours of class work and 5 hours of individual work)
Objective: To use advanced tools and QiSDK API for creating autonomous abilities for the Pepper robot.
- Preparing the working tools (will be studied in advance by students).
- Creating an application for autonomous behavior, motion, and conversation.
- Creating an application for perceptions and knowledge.
* CobiotX: Co – collaboration between robots and humans; Bio – life, emotion, sustainability; Bot – technology, automation and efficiency; IoT – connection, exchange and perception; X – diversity, versatility and Xperience
Design a smart chatbot (20 hours of individual work)
Objective: To design a chatbot using Python, NLP models, and the RASA framework that can assist workers on the job, and provide relevant and efficient information to them in real time.
- Introduction to chatbots and their use in the workplace.
- Overview of the RASA framework and its features.
- Setting up a development environment for the chatbot.
- Understanding NLP models and their use in chatbots.
- Implementing a basic chatbot using Python and RASA.
- Integrating NLP models into the chatbot to enhance its capabilities.
- Training the chatbot on a dataset specific to the job requirements.
- Evaluating the performance of the chatbot and fine-tuning it as needed.
- Deploying the chatbot for use by workers on the job.
- Hands-on exercises and projects reinforce the concepts learned in class.
The students will work on the chatbot individually and will be required to submit a final project at the end of the semester. The aim is to provide students with hands-on experience in designing and building a chatbot that can be used in real-world scenarios to assist workers on the job.
Note: If students do not have prior knowledge of Python, they can design a smart chatbot using other popular programming languages like JavaScript, Ruby, or Java. For those that have no programming skills, no-code platforms can be considered. There are several no-code platforms and frameworks available that can help students design and build chatbots without writing any code. These platforms typically offer a drag-and-drop interface and a visual flow builder that allow the design and deploy chatbots using pre-built components, such as chat widgets, message handlers, and decision trees. Some popular no-code platforms for building chatbots include ManyChat, MobileMonkey, Tars, BotStar, Chatfuel, and Landbot. With these no-code platforms, students can create simple chatbots that can answer frequently asked questions, respond to inquiries, or assist users with specific tasks. However, for more complex chatbots that require advanced functionality, it may still be necessary to write some code.
Design an application with the Furhat robot (optional: 8 hours of additional teamwork in the lab)
Objective: To design an application with the Furhat robot to improve the experience of new employees in a manufacturing enterprise. The aim is to create an interactive training program using the Furhat’s conversational interface to introduce new employees to the production process, company culture and rules, safety procedures, and provide hands-on training. The goal is to enhance the learning experience by making it more engaging, interactive, and personalized, while also collecting feedback from the trainees to further improve the program.
Note: Furhat robot is an AI-powered conversational interface designed to simulate human interaction and help people communicate with technology. The Furhat robot features a screen that displays a human-like face, which can be animated and customized to create realistic conversations with users. It’s typically used in applications where people need to interact with technology in a more natural way.
Use case: A manufacturing enterprise wants to improve new employees’ experience by implementing a conversational interface using the Furhat robot. The application is the robot to interact with the new employees and introduce them to the production process, company culture and rules, safety procedures, and deliver an interactive training program for new employees in the manufacturing company. The program will use the Furhat robot’s face to display various expressions and emotions to create a more engaging and interactive learning experience. The robot would be programmed to ask questions and provide feedback to the trainees, helping them to better understand the processes and procedures involved in the manufacturing process. The trainees could also ask questions and receive answers from the robot, providing a more interactive and hands-on approach to their training.. This could be a highly engaging and interactive way for new employees to learn about the company and their role in it, as well as a cost-effective alternative to traditional training methods. Additionally, the robot could collect feedback from the new employees.
Supporting Infrastructure
To run the activity for this course unit, students will have the possibility to work in our labs with the following technologies:
- Several Aldebaran technologies (4 Nao Robots, 2 Pepper robots)
- Furhat robot
- Computers to run various programming languages
- Industrial robots connected to the cloud with video perception (ABB, Kuka, Dobo Magician)
- Autonomous mobile platforms
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