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5.58. Robotics (Elective)

5.58.1. Justification ↑ Back to top

Robotics is the physical manifestation of Artificial Intelligence. This course introduces the fundamental concepts of robot design, including kinematics, perception, and control. Students will explore how robots interact with the physical world through sensors and actuators, and how they make autonomous decisions using probabilistic mapping and localization techniques. The course bridges the gap between abstract algorithms and physical execution.

5.58.2. Generales Goals ↑ Back to top

1. Understand the mechanical and mathematical foundations of robot kinematics.
2. Master sensor fusion techniques for robot perception.
3. Implement navigation and path planning algorithms.
4. Analyze the integration of machine learning in robotic control.
5. Design and simulate autonomous agents for specific environments.

5.58.3. Contribution to Outcomes ↑ Back to top

AG-C09) Design and Development of Solutions: Designs, implements, and evaluates solutions for complex computing problems. (Usage)

AG-C11) Use of Tools: Applies modern computing tools in problem solving. (Assessment)

5.58.4. Content ↑ Back to top

5.58.4.1. Robotics (12 hours) [Skills AG-C09,AG-C11] ↑ Back to top

Bibliography: (Siegwart et al., 2011; Craig, 2017)

Topics

1. Overview: problems and progress:
2. State-of-the-art robot systems, including their sensors and an overview of their sensor processing
3. Robot control architectures, e.g., deliberative vs reactive control and Braitenberg vehicles
4. World modeling and world models
5. Inherent uncertainty in sensing and in control enumerate
6. Sensors and effectors:
7. Sensors: e.g., LIDAR, sonar, vision, depth, stereoscopic, event cameras, microphones, haptics
8. Effectors: e.g., wheels, arms, grippers enumerate
9. Coordinate frames, translation, and rotation (2D and 3D)
10. Configuration space and environmental maps
11. Interpreting uncertain sensor data
12. Localization and mapping
13. Navigation and control
14. Forward and inverse kinematics
15. Motion path planning and trajectory optimization
16. Manipulation and grasping
17. Joint control and dynamics
18. Vision-based control
19. Multiple-robot coordination and collaboration
20. Human-robot interaction:
21. Shared workspaces
22. Human-robot teaming and physical HRI
23. Social assistive robots
24. Motion/task/goal prediction
25. Collaboration and communication (explicit vs implicit, verbal or symbolic vs non-verbal or visual)
26. Trust enumerate
27. Applications and Societal, Economic, and Ethical Issues:
28. Societal, economic, right-to-work implications
29. Ethical and privacy implications of robotic applications
30. Liability in autonomous robotics
31. Autonomous weapons and ethics
32. Human oversight and control enumerate

Learning Outcomes

1. List capabilities and limitations of today's state-of-the-art robot systems, including their sensors and the crucial sensor processing that informs those systems [List]
2. Integrate sensors, actuators, and software into a robot designed to undertake a specific task [Apply]
3. Program a robot to accomplish simple tasks using deliberative, reactive, and/or hybrid control architectures [Apply]
4. Implement fundamental motion planning algorithms within a robot configuration space [Apply]
5. Characterize the uncertainties associated with common robot sensors and actuators; articulate strategies for mitigating these uncertainties [Characterize]
6. List the differences among robots' representations of their external environment, including their strengths and shortcomings [List]
7. Compare and contrast at least three strategies for robot navigation within known and/or unknown environments, including their strengths and shortcomings [Compare]
8. Describe at least one approach for coordinating the actions and sensing of several robots to accomplish a single task [Describe]
9. Compare and contrast a multi-robot coordination and a human-robot collaboration approach and attribute their differences to differences between the problem settings [Compare]
10. Analyze the societal, economic, and ethical issues of a real-world robotics application [Analyze]

5.58.4.2. Perception and Computer Vision (12 hours) [Skills AG-C09,AG-C11] ↑ Back to top

Bibliography: (Thrun et al., 2005; Siegwart et al., 2011)

Topics

1. Computer vision:
2. Image acquisition, representation, processing, and properties
3. Shape representation, object recognition, and segmentation
4. Motion analysis
5. Generative models enumerate
6. Audio and speech recognition
7. Touch and proprioception
8. Other modalities (e.g., olfaction)
9. Modularity in recognition
10. Approaches to pattern recognition:
11. Classification algorithms and measures of classification quality
12. Statistical techniques
13. Deep learning techniques enumerate

Learning Outcomes

1. Summarize the importance of image and object recognition in AI and indicate several significant applications of this technology [Summarize]
2. List at least three image-segmentation approaches, such as thresholding, edge-based and region-based algorithms, along with their defining characteristics, strengths, and weaknesses [List]
3. Implement 2d object recognition based on contour-based and/or region-based shape representations [Apply]
4. Distinguish the goals of sound-recognition, speech-recognition, and speaker-recognition and identify how the raw audio signal will be handled differently in each of these cases [Distinguish]
5. Provide at least two examples of a transformation of a data source from one sensory domain to another, e.g., tactile data interpreted as single-band 2d images [Generate]
6. Implement a feature-extraction algorithm on real data, e.g., an edge or corner detector for images or vectors of Fourier coefficients describing a short slice of audio signal [Apply]
7. Implement an algorithm combining features into higher-level percepts, e.g., a contour or polygon from visual primitives or phoneme hypotheses from an audio signal [Apply]
8. Implement a classification algorithm that segments input percepts into output categories and quantitatively evaluates the resulting classification [Apply]
9. Evaluate the performance of the underlying feature-extraction, relative to at least one alternative possible approach (whether implemented or not) in its contribution to the classification task (8), above [Evaluate]
10. Describe at least three classification approaches, their prerequisites for applicability, their strengths, and their shortcomings [Describe]
11. Implement and evaluate a deep learning solution to problems in computer vision, such as object or scene recognition [Apply]

5.58.4.3. Robotics (24 hours) [Skills AG-C09,AG-C11] ↑ Back to top

Bibliography: (Thrun et al., 2005; Siegwart et al., 2011)

Topics

1. Overview: problems and progress:
2. State-of-the-art robot systems, including their sensors and an overview of their sensor processing
3. Robot control architectures, e.g., deliberative vs reactive control and Braitenberg vehicles
4. World modeling and world models
5. Inherent uncertainty in sensing and in control enumerate
6. Sensors and effectors:
7. Sensors: e.g., LIDAR, sonar, vision, depth, stereoscopic, event cameras, microphones, haptics
8. Effectors: e.g., wheels, arms, grippers enumerate
9. Coordinate frames, translation, and rotation (2D and 3D)
10. Configuration space and environmental maps
11. Interpreting uncertain sensor data
12. Localization and mapping
13. Navigation and control
14. Forward and inverse kinematics
15. Motion path planning and trajectory optimization
16. Manipulation and grasping
17. Joint control and dynamics
18. Vision-based control
19. Multiple-robot coordination and collaboration
20. Human-robot interaction:
21. Shared workspaces
22. Human-robot teaming and physical HRI
23. Social assistive robots
24. Motion/task/goal prediction
25. Collaboration and communication (explicit vs implicit, verbal or symbolic vs non-verbal or visual)
26. Trust enumerate
27. Applications and Societal, Economic, and Ethical Issues:
28. Societal, economic, right-to-work implications
29. Ethical and privacy implications of robotic applications
30. Liability in autonomous robotics
31. Autonomous weapons and ethics
32. Human oversight and control enumerate

Learning Outcomes

1. List capabilities and limitations of today's state-of-the-art robot systems, including their sensors and the crucial sensor processing that informs those systems [List]
2. Integrate sensors, actuators, and software into a robot designed to undertake a specific task [Apply]
3. Program a robot to accomplish simple tasks using deliberative, reactive, and/or hybrid control architectures [Apply]
4. Implement fundamental motion planning algorithms within a robot configuration space [Apply]
5. Characterize the uncertainties associated with common robot sensors and actuators; articulate strategies for mitigating these uncertainties [Characterize]
6. List the differences among robots' representations of their external environment, including their strengths and shortcomings [List]
7. Compare and contrast at least three strategies for robot navigation within known and/or unknown environments, including their strengths and shortcomings [Compare]
8. Describe at least one approach for coordinating the actions and sensing of several robots to accomplish a single task [Describe]
9. Compare and contrast a multi-robot coordination and a human-robot collaboration approach and attribute their differences to differences between the problem settings [Compare]
10. Analyze the societal, economic, and ethical issues of a real-world robotics application [Analyze]

5.58.5. Bibliography ↑ Back to top

Siegwart, R., Nourbakhsh, I. R., and Scaramuzza, D. (2011). Introduction to Autonomous Mobile Robots. MIT Press, 2nd edition.

Craig, J. J. (2017). Introduction to Robotics: Mechanics and Control. Pearson, 4th edition.

Thrun, S., Burgard, W., and Fox, D. (2005). Probabilistic Robotics. MIT Press.