Comprehensive Course Listing Across All Semesters

Advanced Departmental Elective Courses

These advanced courses build upon foundational knowledge and offer specialized skills required in modern robotics engineering:

• Deep Learning for Robots: This course explores neural networks, convolutional and recurrent architectures tailored for robotic applications. Students learn to implement and train models on real-time sensor data.
• Natural Language Processing in Robotics: Focuses on integrating language understanding into robot behavior, enabling robots to comprehend commands and communicate naturally with users.
• Reinforcement Learning Algorithms: Covers Q-learning, policy gradients, actor-critic methods, and their implementation in robotic systems for adaptive control and decision-making.
• Human-Centered Robotics: Examines the design principles of robots that interact effectively with humans, including user interface design, empathy modeling, and social robot behavior.
• Cognitive Modeling in HRI: Investigates how cognitive science concepts apply to human-robot interaction, focusing on perception, memory, and decision-making in collaborative scenarios.
• Path Planning Algorithms: Teaches algorithms for navigation in dynamic environments, including A*, Dijkstra's, RRT (Rapidly-exploring Random Tree), and optimization techniques.
• SLAM Techniques: Introduces simultaneous localization and mapping using sensors like LiDAR, cameras, and IMUs to enable autonomous navigation.
• Mobile Robotics: Covers mobile robot kinematics, dynamics, control, and applications in logistics, surveillance, and search-and-rescue missions.
• Underwater Vehicle Design: Focuses on designing vehicles for oceanic exploration, including hydrodynamics, propulsion systems, and communication technologies.
• Soft Robotics: Explores flexible materials, bio-inspired mechanisms, and applications in rehabilitation, prosthetics, and delicate manipulation tasks.

Project-Based Learning Philosophy

The department's philosophy on project-based learning emphasizes experiential education, integrating theoretical concepts with practical implementation. Mini-projects span across semesters, beginning with simple design challenges and progressing to complex multi-disciplinary solutions.

Mini-project structure involves:

• Team formation based on interest alignment
• Quarterly milestone reviews
• Peer evaluation and feedback mechanisms
• Presentation at departmental symposiums

The final-year thesis or capstone project requires students to propose, design, develop, and present a significant contribution to robotics. Projects are selected in collaboration with faculty mentors based on student interests and current research areas.

Evaluation criteria include:

• Technical depth of solution
• Innovation and creativity
• Documentation quality
• Presentation clarity
• Impact on real-world problems