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Course Outline
Introduction to Advanced Physical AI
- Overview of advanced Physical AI concepts
- Recent developments and trends in autonomous systems
- Key challenges in designing autonomous systems
Advanced System Design
- Mechanical and electrical design for complex systems
- Integration of advanced sensors and actuators
- Energy management and sustainability
AI Algorithms for Autonomy
- Deep learning for perception and planning
- Reinforcement learning for adaptive control
- Optimization of AI pipelines for real-time decision-making
Real-Time Data Processing and Integration
- Advanced sensor fusion techniques
- Real-time data processing for dynamic environments
- Advanced navigation and obstacle avoidance strategies
Simulation and Validation
- Advanced use of simulation environments
- Modeling and testing complex scenarios
- System validation and performance optimization
Automation and Deployment Strategies
- Programming advanced workflows for automation
- Ensuring reliability and safety in autonomous deployments
- Scalability and maintenance of autonomous systems
Exploring Future Trends and Challenges
- Advances in human-robot interaction and collaboration
- Ethical considerations in autonomous systems
- The future of Physical AI in various industries
Summary and Next Steps
Requirements
- Strong understanding of AI and machine learning concepts
- Proficiency in robotics system design and control
- Experience with programming languages like Python or C++
Audience
- AI researchers
- Robotics experts
- Software engineers
21 Hours
