AI Robotics 100 Questions

A curated roadmap for mastering modern AI-powered robotics — from perception and control to simulation and cognition.

Each question includes:

  • 📚 Theoretical foundation with clear explanations
  • 💻 Runnable open-source code demo (Python/ROS/Simulation)
  • ⏱️ Time-bounded learning (45-60 minutes per question)
  • 🎯 Difficulty-graded progression (Easy → Medium → Hard)

🔍 Introduction

🎯 Motivation

As artificial intelligence (AI) and robotics converge, there is an urgent need for a unified, structured, and hands-on knowledge base that bridges foundational theory with real-world implementation. However, the current learning landscape for AI robotics is fragmented — scattered across academic papers, online courses, ROS tutorials, and open-source repositories with steep learning curves.

Many learners struggle with questions like:

  • What concepts truly matter in AI-driven robotics?
  • Which algorithms are relevant and how do they translate to code?
  • What tools or frameworks should I use to move from simulation to reality?

To address this gap, we present AI Robotics 100 Questions: Theory + Code — a curated, difficulty-ranked collection of the most essential and frequently asked questions in the field, each accompanied by theoretical explanation and runnable code demos.

👥 Who Should Care

This open resource is designed for:

  • 🤖 Students entering the fields of robotics, AI, or embedded systems
  • 💼 Job seekers preparing for interviews in AI robotics or autonomous systems
  • 🧑‍🔬 Researchers & engineers seeking a consolidated reference for practical algorithms
  • 📚 Educators building courses in robotics, AI, or human-robot interaction
  • 🏗️ Makers and hobbyists experimenting with robot design and AI integration

💡 What Benefits It Brings

✅ Structured Learning Path: Questions are categorized into seven key modules and three levels of difficulty (easy, medium, hard), covering everything from perception and control to Sim2Real transfer.

✅ Theory + Practice: Each item is not just a concept — it includes actual code examples (Python, ROS, simulation environments) to bridge the gap between understanding and doing.

✅ Simulation to Reality: Sim2Real is embedded into the curriculum to help learners move beyond simulations and deploy real-world intelligent robots.

✅ Open Collaboration: This is an open-source community project — contributions, extensions, and localizations are highly encouraged.

✅ Industrial-Grade Modules: Each Q&A demo in this series is crafted with a research-level and engineering-grade structure — featuring clearly defined theory, runnable code, performance evaluation, and visual analytics.

🚀 What We Hope to Achieve

We envision this as a living knowledge stack for the next generation of AI robotics education and innovation. With consistent community engagement, we hope to:

  • Build a repository of trusted, executable, and up-to-date knowledge
  • Support a wide range of learners and developers with minimal entry barriers
  • Lay the foundation for autonomous robot cognition by structuring knowledge around both how robots do things and how robots understand tasks
  • Ultimately, this project aims to accelerate learning, experimentation, and innovation in AI robotics — from classroom to lab, from hobby to product, from simulation to the real world.

🎯 Learning Framework

Target Audience

  • 🎓 Graduate Students: Comprehensive curriculum for AI robotics mastery
  • 💼 Industry Professionals: Interview preparation and skill development
  • 🔬 Researchers: Practical reference for algorithm implementation
  • 📚 Educators: Structured course material with hands-on labs
  • 🛠️ Makers: Real-world robot development guidance

Quality Standards

📌 Effort Benchmarking

  • 3 demos ≈ 1 ICRA/IROS-quality robotics research contribution, with reproducible experiments and comparative baselines
  • 10 demos ≈ a startup pitch demo, demonstrating technical feasibility, stack diversity (perception, control, autonomy), and product-ready modularity

These demos are not only educational, but also serve as launchpads for rapid prototyping, benchmarking, reproducibility studies, and even startup MVPs.

🧠 Part 1. Fundamentals of AI Robotics

🟢 Easy

  1. What is AI Robotics, and how does it differ from traditional robotics?
  2. What are the core modules in a modern robotic system?
  3. How to set up a basic robot simulation environment (e.g., Gazebo, Webots)?
  4. What are robot coordinate systems? (Base, Tool, World frames)

🟡 Medium

  1. What is ROS (Robot Operating System), and why is it widely adopted?
  2. What is the difference between forward and inverse kinematics?
  3. What are the key stages in the robot development lifecycle?

🔴 Hard

  1. What is embedded AI, and how is AI deployed on low-power devices?
  2. How do AI systems integrate with traditional control theory?
  3. How do robots leverage cloud and edge computing?

🧲 Part 2. Perception and Sensing

🟢 Easy

  1. How to use a depth camera (e.g., Realsense) to get RGB-D data?
  2. How to recognize and track objects using OpenCV?
  3. How to use IMU data for pose estimation?
  4. How to detect objects by color and shape?

🟡 Medium

  1. How to use YOLO or SSD for real-time object detection?
  2. How to process LiDAR data for mapping?
  3. How to fuse camera and IMU data for VIO (Visual-Inertial Odometry)?
  4. How to implement gesture or voice-based command control?
  5. What is visual servoing, and how is it applied?
  6. How do robots detect ground and obstacles?

🔴 Hard

  1. How does mmWave radar enable robust perception?
  2. What is semantic and instance segmentation in robotic vision?
  3. How to build a multi-modal perception system (vision + depth + audio)?
  4. How does perception uncertainty affect control and navigation?
  5. How is real-time perception used in feedback control?
  6. How to jointly estimate visual odometry and depth?
  7. How to train custom perception models and deploy them on robots?
  8. How do robots infer context from sensor input?

🦾 Part 3. Control and Manipulation

🟢 Easy

  1. What is PID control, and how is it tuned?
  2. How to model and control a differential drive robot in ROS?
  3. How to use forward/inverse kinematics to control robot arms?
  4. What's the difference between open-loop and closed-loop control?

🟡 Medium

  1. How to plan motion trajectories for multi-joint arms?
  2. How do controllers handle dynamic constraints?
  3. How to implement visual feedback-based grasping?
  4. How do robots perform real-time obstacle avoidance?
  5. How to coordinate control of a mobile base with a robotic arm?
  6. How to perform pose and orientation control for end-effectors?

🔴 Hard

  1. How is reinforcement learning used for robot control?
  2. How to implement imitation learning from human demonstrations?
  3. How to solve redundancy and optimize control of high-DOF arms?
  4. What are coordination strategies for multi-robot manipulation?
  5. How to mitigate latency and jitter in real-time control loops?
  6. How to apply LQR or MPC for optimal control and tracking?
  7. How to predict and control multimodal human-robot interactions?

🧭 Part 4. Localization and Navigation

🟢 Easy

  1. How does a robot localize itself in a known map?
  2. How to implement A* and Dijkstra for global path planning?
  3. How to use ROS Navigation Stack for basic autonomous movement?
  4. What is AMCL (Adaptive Monte Carlo Localization), and how does it work?

🟡 Medium

  1. TEB vs. DWA: Which local planner is better for complex environments?
  2. How to use SLAM (Simultaneous Localization and Mapping) for unknown spaces?
  3. How to perform multi-robot localization and map merging?
  4. How to replan paths in dynamic environments?
  5. How to fuse IMU and vision for robust localization?
  6. How does semantic mapping enhance robot navigation?

🔴 Hard

  1. What is POMDP planning under uncertainty?
  2. What is belief-space navigation, and how is it implemented?
  3. How to enable multi-floor navigation and elevator handling?
  4. How do drones localize in GPS-denied environments?
  5. How to compress maps and optimize memory usage?
  6. What's new in ROS 2 navigation architecture?
  7. How to design concurrent systems for real-time map updates?
  8. How do hybrid robots (ground/air/water) manage navigation modes?

🧑‍🤝‍🧑 Part 5. Human-Robot Interaction (HRI)

🟢 Easy

  1. How to control robots using voice commands?
  2. How to use MediaPipe/OpenCV for gesture recognition?
  3. How to process multi-modal inputs (voice + vision)?
  4. How to design basic robot responses in a dialogue?

🟡 Medium

  1. How to integrate multi-turn dialogue systems with behavior trees?
  2. How to use LLMs (e.g., ChatGPT) to interpret human commands?
  3. How to detect human intention and emotional state?
  4. How to model safety zones in physical human-robot interaction?
  5. How to use VR/AR to enhance collaboration and training?
  6. How to translate natural language into robot behavior sequences?

🔴 Hard

  1. How to model users and memory in long-term HRI?
  2. How to support multi-user, multilingual robot interactions?
  3. How to build personalized interaction models in social robots?
  4. How to extract high-level strategies from human demonstrations?
  5. How to build full loops from LLM → plan → execution?

🔮 Part 6. AI Decision-Making and Autonomy

🟢 Easy

  1. What is a finite state machine (FSM), and how is it used in robotics?
  2. What are behavior trees (BT), and how do they compare with FSMs?
  3. How to implement a simple task scheduler?

🟡 Medium

  1. How to train robot policies using reinforcement learning?
  2. How to design an end-to-end perception-decision-control pipeline?
  3. How to map language commands into robot planning modules?
  4. How to handle task failures and recovery in mission execution?
  5. How to allocate tasks in multi-agent systems?

🔴 Hard

  1. How to use LLMs as high-level planners or mission interpreters?
  2. How do agents cooperate or compete in multi-agent environments?
  3. What is brain-inspired AI in robotics, and how is it evolving?
  4. How to architect a general-purpose embodied cognitive agent?

🧪 Part 7. Simulation & Sim2Real Transfer

🟢 Easy

  1. What is a robot simulation environment, and why is it essential?
  2. How to create simple obstacle-avoidance scenes?

🟡 Medium

  1. How to import URDF models into simulation?
  2. How to test planning algorithms in simulation?
  3. How to collect multi-modal data (image, trajectory, language) from simulation?
  4. How to prototype and test behavior trees or FSMs in simulation?

🔴 Hard

  1. What is the Sim2Real gap, and why is it challenging?
  2. How to apply domain randomization to improve real-world transfer?
  3. How to transfer learned policies from simulation to real robots?
  4. What RL/IL algorithms are most robust to Sim2Real (e.g., RL^2, Meta-RL)?
  5. How to design a unified simulation-to-deployment training pipeline?

🎯 Learning Pathways

🚀 Beginner Path (20 questions - 2 months)

Start with Easy questions across all parts to build foundational understanding

🎓 Academic Path (50 questions - 6 months)

Systematic progression through all difficulty levels with research focus

💼 Industry Path (30 questions - 3 months)

Focus on practical applications and deployment-ready solutions

🔬 Research Path (All 100 questions - 12 months)

Complete mastery with advanced topics and cutting-edge techniques

📊 Learning Outcomes

Technical Skills

  • ✅ Python/ROS programming proficiency
  • ✅ Computer vision and sensor processing
  • ✅ Control theory and robotics mathematics
  • ✅ Machine learning and AI integration
  • ✅ System design and architecture

Practical Capabilities

  • ✅ Build complete robotic systems
  • ✅ Deploy AI models on robots
  • ✅ Handle real-world engineering challenges
  • ✅ Integrate multiple technologies
  • ✅ Debug and optimize robot performance

Professional Readiness

  • ✅ Industry interview preparation
  • ✅ Research paper implementation
  • ✅ Startup technical foundation
  • ✅ Open-source contribution capability
  • ✅ Teaching and mentoring skills

🛠️ Implementation Standards

Code Quality

  • Clean, documented, production-ready implementations
  • Performance benchmarks and analysis
  • Error handling and edge cases
  • Modular, extensible architecture

Educational Value

  • Clear theoretical explanations
  • Step-by-step implementation guides
  • Real-world application context
  • Comparative analysis and trade-offs

Reproducibility

  • Consistent development environment
  • Deterministic results and metrics
  • Version-controlled implementations
  • Community testing and validation

🌟 Success Metrics

Upon completion of this curriculum, learners will have:

  • 📈 100+ Working Demos: Complete portfolio of robotics implementations
  • 🎯 Industry Readiness: Sufficient knowledge for robotics engineering roles
  • 🔬 Research Foundation: Capability to contribute to academic research
  • 🚀 Innovation Capacity: Skills to develop novel robotic solutions
  • 🤝 Community Impact: Ability to teach and mentor others in robotics

🚀 Get Involved

We welcome:

  • Code contributions
  • Pull requests for new questions or fixes
  • Your open-source demos & real-robot examples
  • Community feedback and suggestions
  • Translation and localization efforts

Let's build the AI Robotics Knowledge Stack — together.

📜 License

This project is licensed under the MIT License.

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

This curriculum represents the essential knowledge required for modern AI robotics, from foundational concepts to cutting-edge research topics. Each question builds upon previous knowledge while introducing new challenges and applications.

Ready to start your AI Robotics journey? Begin with Question 1! 🚀

Last modified on: Jul 15, 2025
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