Chapter 1. Perception & State Estimation

Section 1. Visual–Inertial SLAM in Simulation

• Simulation environment and sensor configuration
• Camera and IMU noise modeling
• Feature extraction and tracking
• Visual–inertial fusion pipeline
• Map construction and keyframe management
• Loop closure detection
• Drift accumulation and correction analysis
• Performance evaluation metrics

Section 2. Multi-Sensor Kalman Filtering (EKF vs UKF)

• System state definition and motion model
• Sensor observation models
• Linearization assumptions and limitations
• EKF implementation and tuning
• UKF implementation and sigma-point selection
• Filter consistency and divergence cases
• Comparative accuracy and stability analysis

Section 3. Classical Geometry vs Learned Perception

• Problem formulation and benchmark tasks
• Classical geometric perception pipeline
• Neural perception model selection
• Training data generation in simulation
• Inference latency and resource usage
• Accuracy, robustness, and failure analysis

Section 4. Loop Closure and Drift Correction

• Drift sources in long-horizon SLAM
• Place recognition methods
• Pose graph construction
• Optimization and constraint solving
• Consistency enforcement
• Quantitative drift reduction results

Section 5. Domain Randomization for Perception Robustness

• Parameter randomization design
• Dataset generation across domains
• Training and validation procedures
• Generalization to unseen environments
• Sensitivity analysis
• Failure case taxonomy

Section 6. Observability and Degeneracy in Localization

• State observability analysis
• Degenerate motion patterns
• Rank-deficient estimation scenarios
• Simulation-based validation
• Mitigation strategies
• Implications for real-world deployment

Chapter 2. Planning & Decision-Making

Section 1. Sampling-Based Motion Planning (RRT, RRT*, PRM)

• Problem definition and configuration space modeling
• Collision checking and environment representation
• Tree and roadmap construction strategies
• Sampling bias and heuristic guidance
• Path optimization and convergence behavior
• Computational complexity and scalability analysis

Section 2. Trajectory Optimization Methods

• Trajectory parameterization and discretization
• Cost function formulation
• Constraint handling and feasibility enforcement
• Gradient-based vs sampling-based optimization
• Initialization sensitivity and local minima
• Performance and solution quality evaluation

Section 3. Belief-Space Planning Under Uncertainty

• State uncertainty representation
• Propagation of belief through dynamics
• Observation models and information gain
• Planning in belief space
• Approximation and tractability trade-offs
• Evaluation under partial observability

Section 4. Task-and-Motion Planning Integration

• Symbolic task representation
• Continuous motion planning interfaces
• Hierarchical decomposition strategies
• Failure recovery and replanning
• Temporal and resource constraints
• End-to-end execution evaluation

Section 5. Risk-Aware and Chance-Constrained Planning

• Risk metrics and probability models
• Chance constraint formulation
• Uncertainty propagation in planning
• Trade-offs between safety and optimality
• Scenario-based evaluation
• Robustness assessment

Section 6. Planning in Dynamic and Adversarial Environments

• Modeling dynamic obstacles and agents
• Prediction of environment evolution
• Online replanning strategies
• Adversarial scenario generation
• Stability and responsiveness analysis
• Comparative performance metrics

Chapter 3. Control & Dynamics

Section 1. Rigid-Body Dynamics Modeling in Simulation

• System modeling assumptions and scope
• Coordinate frames and kinematic chains
• Inertia, mass, and center-of-mass specification
• Forward and inverse dynamics computation
• Numerical integration and stability considerations
• Model validation against reference behaviors

Section 2. Inverse Dynamics Control

• Control objectives and formulation
• Feedforward and feedback components
• Constraint handling and actuator limits
• Sensitivity to modeling errors
• Performance under varying loads
• Tracking accuracy evaluation

Section 3. Model Predictive Control for Robotics

• Prediction model formulation
• Horizon selection and discretization
• Cost function and constraint design
• Real-time optimization strategies
• Computational burden analysis
• Closed-loop performance assessment

Section 4. Nonlinear and Adaptive Control Methods

• Nonlinear system characterization
• Controller design techniques
• Parameter adaptation mechanisms
• Robustness to disturbances
• Convergence and stability analysis
• Comparative control performance

Section 5. Hybrid and Switched Control Systems

• Hybrid system modeling
• Mode switching logic
• Stability across switching events
• Guard conditions and transitions
• Failure scenarios and recovery
• Validation in multi-mode tasks

Section 6. Stability Analysis and Failure Modes

• Stability criteria and definitions
• Lyapunov-based analysis
• Numerical stability testing
• Failure mode identification
• Stress testing under extreme conditions
• Interpretation of stability margins

Chapter 4. Learning-Based Robotics

Section 1. Reinforcement Learning for Continuous Control

• Problem formulation and reward design
• State and action space definition
• Policy and value function representation
• Training in simulation environments
• Convergence behavior and instability cases
• Policy evaluation and benchmarking

Section 2. Sample Efficiency and Exploration Strategies

• Exploration–exploitation trade-offs
• On-policy vs off-policy learning
• Replay buffers and data reuse
• Curriculum learning and task shaping
• Measuring sample efficiency
• Failure cases and overfitting analysis

Section 3. Imitation Learning and Behavioral Cloning

• Expert data generation
• Dataset aggregation methods
• Supervised policy training
• Distribution shift and compounding errors
• Performance comparison to reinforcement learning
• Generalization assessment

Section 4. Learning Forward and Inverse Dynamics Models

• Model structure and representation
• Training data collection
• Prediction accuracy evaluation
• Integration with control and planning
• Error accumulation and stability issues
• Comparative model performance

Section 5. Sim-to-Real Gap Quantification

• Sources of simulation mismatch
• Parameter sensitivity analysis
• Robust training strategies
• Transfer performance metrics
• Failure diagnosis
• Implications for real-world deployment

Section 6. Policy Generalization and Overfitting

• Generalization criteria
• Training diversity requirements
• Regularization techniques
• Evaluation on unseen tasks
• Overfitting indicators
• Mitigation strategies

Chapter 5. Multi-Robot & Swarm Systems

Section 1. Decentralized Formation Control

• Problem formulation and formation specifications
• Relative vs absolute positioning schemes
• Control laws for formation maintenance
• Scalability with increasing agent count
• Disturbance and noise sensitivity
• Formation stability evaluation

Section 2. Consensus and Distributed Optimization

• Consensus problem definition
• Communication graph modeling
• Distributed update rules
• Convergence conditions
• Effects of delays and packet loss
• Performance and convergence analysis

Section 3. Multi-Agent Reinforcement Learning

• Joint vs decentralized learning formulations
• Credit assignment strategies
• Non-stationarity and training instability
• Cooperative and competitive task setups
• Policy convergence behavior
• Emergent coordination analysis

Section 4. Communication-Constrained Coordination

• Communication bandwidth modeling
• Information sharing strategies
• Local vs global coordination trade-offs
• Robustness to communication failures
• Performance under constrained channels
• Comparative coordination outcomes

Section 5. Cooperative Task Allocation

• Task representation and decomposition
• Auction-based allocation mechanisms
• Distributed negotiation protocols
• Dynamic task reassignment
• Efficiency and optimality measures
• Scalability assessment

Section 6. Emergent Behaviors in Large-Scale Swarms

• Local interaction rule design
• Self-organization mechanisms
• Pattern formation and phase transitions
• Robustness to agent loss
• Sensitivity to parameter variation
• Quantitative emergence metrics

Chapter 6. Safety, Robustness & Verification

Section 1. Failure Injection and Fault Simulation

• Identification of critical failure modes
• Sensor fault modeling and injection
• Actuator degradation and delay simulation
• Partial observability scenarios
• Cascading failure analysis
• System response characterization

Section 2. Robust Control Under Uncertainty

• Uncertainty modeling in dynamics and sensing
• Robust control objectives
• Worst-case disturbance analysis
• Robust controller design methods
• Performance degradation assessment
• Robustness margin evaluation

Section 3. Safety-Constrained Planning and Control

• Safety specification and constraint definition
• Hard vs soft safety constraints
• Constraint enforcement mechanisms
• Trade-offs between safety and performance
• Runtime constraint monitoring
• Safety violation analysis

Section 4. Adversarial Attacks on Robotic Systems

• Threat model definition
• Adversarial perception perturbations
• Policy manipulation and spoofing attacks
• System resilience testing
• Detection and mitigation strategies
• Impact assessment on task performance

Section 5. Formal Verification of Robotic Controllers

• Formal model abstraction
• Reachability and invariance analysis
• Verification toolchain setup
• Scalability limitations
• Counterexample generation
• Verification outcome interpretation

Section 6. Runtime Monitoring and Safety Envelopes

• Runtime state monitoring architectures
• Safety envelope definition
• Anomaly detection mechanisms
• Intervention and override strategies
• Latency and responsiveness analysis
• Evaluation under stress scenarios