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Physical AI & Humanoid Robotics

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04:57 AM

Topic 3 — Performance Targets, Constraints & Capstone Scope

This topic builds on the research in specs/1-physical-ai-course/research.md and explains how fast your system needs to be, what limits you must design around, and how far the capstone project is expected to go.

It translates raw technical findings into concrete expectations for your labs and final project.

1. Performance Goals: Real-Time Robotics​

Robotics is not offline data science. Your humanoid must respond in real time:

  • Control loops:
    • Joint controllers and balance logic must update at high rate (tens to hundreds of Hz).
    • Latency spikes can cause instability, oscillations, or falls.
  • Sensor processing:
    • RGB‑D, IMU, and LiDAR data must be ingested and processed fast enough to inform control.
    • Perception pipelines should keep up with camera frame rates (e.g., 30–60 FPS where feasible).
  • Simulation frame rate:
    • Target ≈ 60 FPS in Gazebo or Isaac Sim for smooth, realistic physics and training data.
  • Natural language understanding:
    • Voice commands (Whisper + LLM) should feel responsive enough for interactive use.

In practice, this means:

  • Monitoring latency and frame rates during labs.
  • Being careful with heavy GPU/CPU workloads (e.g., large models, dense point clouds).
  • Degrading gracefully when resources are tight (simpler models, lower resolution, throttled rates).

2. Practical Constraints: Hardware, Resources & Ethics​

The course assumes real constraints you would face in a professional robotics lab:

  • Shared GPUs and robots:
    • You don’t own the entire workstation or robot fleet.
    • Schedule simulation runs thoughtfully and avoid monopolizing lab hardware.
  • Sensor availability:
    • Limited numbers of RealSense cameras, IMUs, and other sensors.
    • Labs are designed so multiple students can share sensor rigs and replay recorded data when necessary.
  • Safety and ethical responsibility:
    • Physical robots can cause harm if misused.
    • Controllers must be tested in simulation first and include safe defaults, limits, and abort paths.
    • Transparency and beneficial outcomes (as emphasized in the Constitution) are part of “done”, not extras.

These constraints force you to think like a real robotics engineer: design for robustness, fairness of resource use, and safety from the start.

3. Hybrid Architecture: Simulation + Physical Deployment​

The capstone system is explicitly hybrid:

  • Simulation side (Digital Twin Workstation):
    • Runs on Ubuntu 22.04 with an RTX‑class GPU.
    • Hosts ROS 2, Gazebo, Isaac Sim, and heavy perception or training workloads.
    • Used to prototype behaviors, train models, and generate synthetic data.
  • Physical side (Edge Kit / Jetson Orin):
    • Runs ROS 2 nodes and lightweight models on resource‑constrained hardware.
    • Connects to RealSense, microphones, and (when available) humanoid platforms.

This sim‑to‑real split supports the Constitution’s Simulation‑First and Sim‑to‑Real principles:

  • You debug in simulation first, where failures are safe and cheap.
  • Only then do you deploy to the edge kit and, where available, real humanoid hardware.

4. Capstone Scope: What “Autonomous Humanoid” Really Means​

The research defines a clear but ambitious scope:

  • Voice‑driven commands:
    • Whisper and LLMs interpret natural language (e.g., “pick up the cup”).
  • Navigation and path planning:
    • Nav2 and ROS 2 navigation stacks plan collision‑free paths in simulated environments.
  • Perception and object recognition:
    • RealSense‑based perception (RGB‑D + IMU) supports SLAM and object detection.
  • Manipulation and interaction:
    • Controllers command arms/hands (or proxies in simulation) to grasp and move objects.

You are not expected to solve all open problems in humanoid robotics, but you are expected to:

  • Integrate the three pillars: ROS 2, digital twins, and VLA.
  • Demonstrate end‑to‑end behavior in simulation, with a clear path to physical deployment.
  • Respect real‑time constraints and safety limits.

5. How This Affects Your Design Decisions​

When choosing algorithms, models, and architectures throughout the course, keep these research‑driven realities in mind:

  • Prefer robust, well‑supported libraries (Nav2, Isaac ROS) over fragile custom hacks.
  • Target real‑time behavior from the beginning; don’t assume “we’ll optimize later”.
  • Design for sim‑to‑real transfer:
    • Use the same ROS 2 interfaces on workstation and Jetson.
    • Keep hardware abstractions clean so you can swap sensors or robots.
  • Always weigh performance vs. complexity vs. safety in line with the project Constitution.

This topic, together with the Tech Stack topic, should guide your engineering decisions for every lab and for the final autonomous humanoid capstone.