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Hardware Considerations for Physical AI

This chapter outlines essential hardware considerations for developing and deploying Physical AI systems, with a focus on humanoid robotics.

Robotic Platforms​

Humanoid Robots​

[Discuss general characteristics and requirements for humanoid robots in the context of Physical AI.]

Key Components​

  • Actuators: Motors, servos, and other mechanisms for movement.
  • Sensors: Cameras (RGB, Depth, Stereo), LiDAR, IMUs, force sensors, tactile sensors.
  • Embedded Systems / Single Board Computers (SBCs): NVIDIA Jetson series (Orin Nano, Xavier), Raspberry Pi, etc.

Processing Units​

GPUs for AI/ML​

[Explain the importance of GPUs for accelerated AI/ML computations, especially for perception, planning, and simulation.]

CPUs for Control​

[Discuss the role of CPUs for real-time control, ROS 2 node management, and general-purpose computing.]

Development Hardware​

Workstations​

[Recommendations for development workstations capable of running simulations and training models.]

Cloud Resources​

[Briefly discuss the use of cloud-based GPUs for more intensive training or large-scale simulations.]

Connectivity and Communication​

Network Requirements​

[Discuss network bandwidth, latency, and reliability for robot-to-controller communication and cloud connectivity.]

ROS 2 Hardware Abstraction​

[Explain how ROS 2 abstracts hardware details, allowing for flexible integration of different robotic components.]

Power Management​

[Considerations for battery life, power consumption, and thermal management in mobile and humanoid robots.]