Performance, Constraints, and Ethics in Physical AI

Chapter 1 Topic 3 focuses on how the course’s technical choices must perform in the real world, and why constraints and ethics are built into the design from day one.

1. Performance Targets for Real Robots

Physical AI systems don’t just need to be “correct” in theory; they must be fast and stable enough to control hardware safely:

• Real‑time control loops (p95 latency)
  Controllers that stabilize a humanoid’s balance or regulate joint torques must run at high frequency with tight latency bounds. Occasional spikes can mean trips, falls, or damaged hardware.

• Real‑time sensor processing
  Camera frames, depth images, point clouds, and IMU streams arrive continuously. Your perception stack must keep up so that downstream planners never act on stale world models.

• 60 FPS simulation
  High‑frame‑rate simulation in Gazebo or Isaac Sim gives you more accurate physics and smoother motion, and produces better training and validation data. If your simulation chugs, your controllers and perception models see an unrealistic world.

• Fast natural language understanding
  Whisper and LLM‑based intent parsing should respond quickly enough for interactive use. Multi‑second delays are acceptable for offline tools, but conversational robots need tight, predictable response times.

In this course, you’ll learn to profile these components and reason about where latency matters most (e.g., control loops and perception) versus where you can tolerate slower, batched workloads.

2. Storage and Data: Why “N/A” Is a Choice

The research explicitly sets “Storage: N/A” for the core humanoid stack:

• The robot primarily manipulates transient data: sensor frames, control commands, and short‑lived internal states.
• Long‑term artifacts—logs, datasets for training, experiment results—can be stored in files or external systems, decoupled from the real‑time control loop.

This design has several implications:

• The ROS 2 graph stays focused on low‑latency messaging, not heavy transactional workloads.
• You avoid coupling robot safety to database availability.
• When you do log data, you do it intentionally: recording topics during experiments, exporting traces for offline analysis, and using them in your learning pipelines.

Throughout the capstone, you’ll be encouraged to ask: Does this really need to be persisted? If yes, can I keep it out of the real‑time path?

3. Real‑World Constraints You Must Design For

The course treats constraints as first‑class design inputs, not after‑the‑fact annoyances:

• Shared, limited hardware
  GPUs, edge kits, and robots are shared resources. Your solutions must run efficiently and degrade gracefully when compute or bandwidth is tight.

• Physical wear, risk, and downtime
  Every experiment has a non‑zero chance of breaking hardware. That is why simulation, staged deployment, and conservative safety limits are part of the normal workflow.

• Timing and variability
  Real environments introduce jitter, network delays, mechanical backlash, and sensor noise. Designs that are only stable in “perfect lab conditions” are considered incomplete.

You’ll see these constraints concretely in lab rubrics and project requirements: it’s not enough to “make it work on my machine”—it has to work reliably on shared lab infrastructure and physical robots.

4. Ethics and Responsibility in Humanoid Systems

Humanoid robots operate close to humans, often in safety‑critical contexts. The research phase emphasizes ethical responsibility alongside technical goals:

• Safety first
  Fail‑safe defaults, conservative force limits, and clear emergency stop behaviors are non‑negotiable. “It usually doesn’t crash” is not an acceptable standard around people.

• Transparency and explainability
  When a humanoid takes an action—especially one driven by an LLM‑based planner—users and operators should be able to understand why. Logging, visualization tools, and clear internal interfaces all support this.

• Beneficial outcomes
  Capstone projects are framed around assisting tasks—logistics, inspection, caregiving support—not replacing human agency or creating opaque surveillance systems.

You will be asked to justify design decisions in light of these principles: How does this choice affect safety? What happens on failure? Who is in control?

5. Hybrid by Design: Simulation and Deployment Together

Finally, the research describes the project as a hybrid system: always thinking about both simulation and physical deployment:

• Simulation (Gazebo, Isaac Sim) is your primary sandbox.
• The Physical AI Edge Kit and optional humanoid hardware are your reality check.

The core workflow is:

1. Design and test behaviors in high‑fidelity digital twins.
2. Validate performance, robustness, and safety conditions in simulation.
3. Migrate to edge hardware with clear expectations about what may change (sensors, latency, friction, unmodeled contacts).

By the end of this chapter, you should see performance, constraints, and ethics not as extra checkboxes, but as the context in which all technical work lives. The rest of the book will build specific tools—perception, planning, control, and VLA—within these boundaries.