Series Index
1- Humanoid Civilization Shift Driven by Physical AI: The Emergence of “i-Human (AI-Human) Productivity.”
2- Human Reallocation: Laborers Disappear, Leaving “Architects” and “Governors.”
3- The Essential Conditions for Humanoids: System Requirements for Power, Safety, Autonomy, and Control
4- Expanding Forms, Differentiating Roles: An Evolution Map of Humanoids by Industry
5- The Growth Mechanism: The “i-Human (AI-Human) Economy,” Where Productivity Meets Knowledge
6- The Technology Inflection Point: How Sensors, Motors, Control, and Algorithms Are Reshaping the Physical AI Stack
By Im Byoung-min (IBM) Columnist
Once, I was on a flight to New York to attend the inauguration of the governor of Pennsylvania. As I disembarked, I thanked the pilot and flight attendants for getting us there safely. I also felt a deep gratitude toward the airplane itself, even though it is an unconscious object. Like airplanes, humanoid robots—like other human-made systems—can be understood as artifacts shaped by human ethics.
This attitude of gratitude toward an object is not necessarily tied to an obligation owed by the object. Rather, the emotional awareness that fosters ethical sensibilities is linked to something fundamental in human nature. Our gratitude for an object is, in essence, a recognition that we are not alone. The connection between objects created through countless people’s work and the labor of others contributes to society, pointing to the relational nature of ethics. The humanoid era may deepen that recognition of interconnectedness, where objects and ethics connect to the human heart.
Human-made airplanes, like other high-speed technologies, can be described through their relationship with humans: they extend human capability by applying speed and scale to what the human body and mind cannot do alone. An airplane performs physically demanding work at speeds beyond unaided human ability, while keeping passengers safe through a chain of human judgment, training, procedures, and engineered systems. The airplane, as an object, cannot feel emotion. Yet the system is designed to earn human trust through reliability and layered safety, enabling safe arrival.
In that sense, a humanoid robot—another system designed to extend human capability through speed and scale—requires robust foundations in power, safety, autonomy, and control. Each of these elements is essential on its own, and they are also tightly interconnected. Below is a detailed look at how each system works and how the robot can operate effectively in real environments.
First, the power system is a core requirement for continuous operation. A stable power supply is necessary for all components—sensors, actuators, motors, compute modules, and communications—to function properly. If power fails, the robot stops regardless of how advanced its AI may be. For that reason, battery life, thermal limits, and power efficiency must be addressed early in design. Power architectures for long-duration operation, charging strategies, and fail-safe or emergency modes that activate during power instability are also critical.
Second, safety systems are among the most important requirements for robots that interact with humans. Unexpected changes in robot behavior can cause physical harm. Safety systems therefore include collision detection and avoidance, emergency stop mechanisms, and limits on force, torque, and speed. Robots must detect obstacles and people and respond immediately when risk is identified, maintaining safety during close-range physical interaction. In workplaces, safety must prioritize human life, and psychological safety matters as well: people should be able to anticipate the robot’s movements, understand its intent, and feel secure around it.
Third, autonomy systems enable a humanoid robot to perform tasks with minimal human intervention. Robots perceive the environment in real time through multiple sensors and choose actions based on that information. When a robot picks up an object, for example, it must localize itself, avoid obstacles, plan a motion path, and complete the task efficiently. Autonomy therefore depends on the ability to interpret surroundings, adapt to changing conditions, and solve problems. It also requires flexible responses to unexpected situations—errors, uncertainties, and surprises that occur outside controlled laboratory settings.
Fourth, the control system must precisely coordinate and regulate the robot’s movements. When a humanoid performs physical work, it must synchronize sensors, motors, joint actuators, and feedback signals to carry out intended actions accurately. Precise control is essential for stable balance, safe manipulation, and reliable locomotion. A well-designed control system integrates motor control, sensor fusion, state estimation, and motion planning, while continuously monitoring movement to ensure smooth, stable operation across components.
A range of engineering tools can support learning and development across these four prerequisite areas—power, safety, autonomy, and control. MATLAB and Simulink are widely used for modeling and designing power-management strategies, helping engineers simulate energy consumption and evaluate efficient power distribution across components. LTspice is often used for circuit-level simulation and analysis, which can support early-stage exploration of power electronics and related subsystems.
For safety, many teams rely on structured engineering methodologies and verification workflows. The V-Model is commonly used as a systems engineering framework for organizing requirements, design, verification, and validation across hardware and software. Safety-oriented development can also be supported by simulation and verification toolchains that help test real-time behavior, fault responses, and boundary conditions—especially when robots must operate in human environments.
For autonomy, the Robot Operating System (ROS) remains one of the most widely used platforms for integrating perception, planning, and motion. ROS supports sensor data handling, mapping, localization, and planning pipelines that are central to autonomous operation. Deep learning frameworks such as TensorFlow and PyTorch are often used to train perception systems—vision, grasp detection, scene understanding—and to support policy learning for decision-making. Reinforcement-learning environments such as OpenAI Gym can provide structured settings for training policies, testing behaviors, and iterating on reward-driven learning strategies.
For control systems design, MATLAB/Simulink toolboxes are commonly used for modeling dynamic systems and designing controllers for position tracking, velocity control, and stability. In ROS-based stacks, control frameworks can be used to implement motion control and coordinate joint-level actuation. Physics simulators such as PyBullet can simulate robot dynamics and contact behavior, enabling teams to test control algorithms and evaluate stability before deploying on real hardware. CoppeliaSim (formerly V-REP) is another comprehensive simulation platform that supports kinematics, dynamics, sensors, and control logic within a unified environment.
Across autonomy and control, simulation plays a central role in building systems safely and efficiently. Gazebo, often used alongside ROS, provides a virtual environment for testing perception pipelines, autonomy behaviors, and control strategies under varied conditions. By modeling realistic environments and evaluating behavior before deployment, teams can iterate on power constraints, safety responses, autonomy performance, and control robustness with reduced risk. Ultimately, selecting the right tool for each development objective—and using it with clear requirements and testing discipline—is crucial for designing and validating the power, safety, autonomy, and control systems that humanoid robots depend on.
