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Humanoid Robotics Engineering: Locomotion, Manipulation, and Whole-Body Control Systems
Paperback
Series: Industrial, Robotics & Digital Infrastructure Engineering
Publisher: Independently Published
Published: Jun 2 2026
Pages: 90
Weight: 0.29
Height: 0.19 Width: 6.00 Depth: 9.00
Language: English
Humanoid robots are rapidly transitioning from academic research laboratories to real-world commercial deployment. Humanoid Robotics Engineering is the ultimate practical guide for engineers, researchers, and mechatronics specialists who want to design, integrate, and deploy advanced bipedal systems.
This comprehensive handbook bridges the gap between theoretical robotics and physical hardware implementation. It provides the deep technical blueprints, equations, and control architectures required to achieve stable bipedal locomotion, precise manipulation, and intelligent environment interaction.
Inside this engineering blueprint, you will master:
- Actuator Dynamics & Hardware: BLDC motors and series elastic actuator design for high-performance whole-body torque control.
- Bipedal Locomotion: Practical implementation of Zero-Moment Point (ZMP) theory and Model Predictive Control (MPC).
- Reinforcement Learning: Accelerate locomotion training using cutting-edge physics engines like Isaac Gym and MuJoCo.
- Whole-Body Control (WBC): Mathematical optimization frameworks for simultaneous balancing, walking, and manipulation.
- Dexterous Manipulation: Grasp planning, force/torque feedback loops, and multi-fingered tactile sensing systems.
- Perception & Navigation: Real-time SLAM, 3D object detection, and spatial understanding pipelines.
- Safety Architecture: Active torque limits, collision mitigation, fall recovery, and human-robot collaboration.
- Validation Workflows: Real-time OS (RTOS) design, hardware-in-the-loop (HIL) testing, and field deployment.
Whether you are an active robotics engineer, a control systems designer, or a mechatronics specialist, this guide delivers the practical methodologies needed to solve the toughest challenges in physical AI. Equip yourself with the expertise to build the next generation of humanoid machines.
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