Part IX: Manipulation, Locomotion, and Embodied Skills

This chapter treats robotic manipulation as object-state control under geometry, contact, friction, and uncertainty.

• 42.1 What manipulation is; reaching and pushing
• 42.2 Pick-and-place pipelines
• 42.3 Contact-rich interaction
• 42.4 Perception for manipulation
• 42.5 Learning manipulation policies (IL, RL, VLA)
• 42.6 Failure detection and recovery
• 42.7 Mobile manipulation: base, arm, perception, and recovery

This chapter studies grasping and dexterity as the design of useful contact sets over time.

• 43.1 Grasp synthesis: analytic and learned (Dex-Net lineage)
• 43.2 Parallel-jaw vs. multi-finger hands
• 43.3 In-hand manipulation and reorientation
• 43.4 Dexterous RL with demonstrations
• 43.5 Sim-to-real for dexterity

This chapter develops touch as a missing modality for contact: DIGIT, GelSight, AnySkin, tactile simulation, slip detection, and multi-modal policies.

• 44.1 Why touch matters for contact-rich tasks
• 44.2 Vision-based tactile sensors (GelSight, DIGIT)
• 44.3 Simulating touch (e.g., tactile sim in Isaac)
• 44.4 Visuo-tactile pretraining and policies
• 44.5 Combining vision and touch

This chapter develops wheeled and legged mobility, balance, gait, massively parallel RL, terrain adaptation, energy, and sim-to-real safety.

• 45.1 Wheeled, legged, and hybrid robots
• 45.2 Balance, stability, and gait
• 45.3 Learning locomotion with massively parallel RL
• 45.4 Terrain adaptation, parkour, and rapid motor adaptation
• 45.5 Energy efficiency; sim-to-real and safety in locomotion

This chapter develops humanoids as whole-body embodied agents: platforms, operational-space control, retargeting, teleoperation, foundation models, and human-scale safety.

• 46.1 Why humanoids became the focus (data, morphology, hardware cost)
• 46.2 Platforms: Unitree G1/H1, Figure, Optimus, 1X, electric Atlas, Apptronik
• 46.3 Whole-body and operational-space control
• 46.4 Learning from humans: HumanPlus, OmniH2O/HOVER, motion retargeting
• 46.5 Teleoperation for humanoids
• 46.6 Dual-system humanoid foundation models
• 46.7 Safety for human-scale robots
• 46.8 Advanced humanoid dynamics and contact mechanics
• 46.9 Boston Dynamics-style loco-manipulation research track

This chapter develops aerial embodied AI through flight dynamics, PX4, ROS 2, MAVLink, obstacle avoidance, coverage, coordination, regulation, and simulation.

• 47.1 Why aerial agents are special
• 47.2 Flight dynamics intuition
• 47.3 Perception, navigation, and obstacle avoidance
• 47.4 Coverage and inspection; multi-drone coordination
• 47.5 Safety, regulation, and simulation for aerial agents
• 47.6 Quadrotor dynamics and flight control
• 47.7 Trajectory generation and GPS-denied missions
• 47.8 PX4 to hardware: SITL, HITL, logs, and flight-test evidence

This chapter develops autonomous driving as embodied AI: sensors, fusion, prediction, planning, world models, CARLA, CommonRoad, scenarios, and safety cases.

• 48.1 Driving as perception, prediction, planning, control
• 48.2 Sensors and sensor fusion in AVs
• 48.3 Detection, lane and behavior prediction
• 48.4 Route and local planning
• 48.5 End-to-end and world-model driving
• 48.6 Scenario testing and safety cases
• 48.7 Vehicle kinematics, dynamics, and control
• 48.8 Route, behavior, and scenario-based planning
• 48.9 Closed-loop driving evaluation and safety assurance