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Physical AI & Humanoid Robotics

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Topic 1 — Foundations of Autonomy & Agent-Based Robotics

Perception, mapping, and control are necessary but not sufficient for autonomy. This topic defines what it means for a humanoid robot to be autonomous, introduces the architecture of an agent-based robotics system, and explains how high-level reasoning, planning, and control fit together.

1.1 What Makes a Robot Autonomous?​

An autonomous robot is more than a remote-controlled machine. It must:

  • Perceive its environment (state awareness).
  • Decide what to do next (planning and decision-making).
  • Act on those decisions through motors and actuators.
  • Adapt to changes and recover from failures without human intervention.

Key distinctions:

  • Teleoperated robot:
    • Human operator issues low-level commands ("move joint 3", "turn left 10 degrees").
    • Robot has little or no independent decision-making.
  • Autonomous robot:
    • Receives high-level goals ("deliver this object to room B").
    • Decides how to achieve them given its current state and environment.

Autonomy requires:

  • A state estimate of the world (from perception and SLAM).
  • A notion of goals and constraints.
  • A policy or planning mechanism to choose actions.
  • A way to monitor execution and react to deviations.

1.2 Architecture of an Autonomous Agent​

A common pattern for agentic robotics systems is a hierarchical stack:

  1. High-Level Reasoning (LLM / Task Planner)
    • Interprets natural language commands.
    • Decomposes goals into sub-tasks or skills.
    • Chooses strategies (e.g., "search room", "replan route").
  2. Task Planner / Behavior Manager
    • Represents tasks as graphs, behavior trees, or finite state machines.
    • Sequences skills, handles branching logic, and implements retry/fallback.
  3. Skill Layer
    • Encapsulates reusable behaviors such as:
      • Navigate to pose.
      • Pick up object.
      • Follow person.
      • Place object at target.
    • Each skill has a defined API (inputs, outputs, success/failure conditions).
  4. Low-Level Control
    • Executes trajectories and control laws:
      • Base navigation controllers.
      • Arm and hand controllers.
      • Balance and compliance controllers.
    • Runs at high frequency (tens to hundreds of Hz).
  5. Sensing and World Model
    • Maintains maps, detections, and state estimates.
    • Feeds into all higher layers for decision-making.

These components form a closed loop:

  • High-level goals → plans → skills → motor commands → new sensor data → updated world model → revised decisions.

1.3 Roles of Each Layer​

High-Level Reasoning (LLM / VLM)​

Responsibilities:

  • Interpret human commands (e.g., "bring me the red mug from the kitchen").
  • Translate them into structured task descriptions:
    • Objectives (what to achieve).
    • Constraints (avoid the wet floor).
    • Preferences (fastest vs safest path).

Constraints:

  • Non-real-time: may be relatively slow and not suitable for millisecond-level decisions.
  • Should not directly command actuators; instead, it generates plans and task graphs.

Task Planner / Behavior Manager​

Responsibilities:

  • Turn high-level tasks into sequences of skills.
  • Handle:
    • Success paths.
    • Failure branches (e.g., object not found).
    • Parallel tasks (scan while walking).

Tools:

  • Behavior trees.
  • Task graphs.
  • Hierarchical finite state machines.

Skills​

Responsibilities:

  • Provide reusable building blocks with clear contracts.
  • Encapsulate:
    • Perception queries (e.g., "where is object X?").
    • Planning calls (e.g., "plan route to Y").
    • Control calls (e.g., "execute grasp").

Example skills:

  • navigate_to_pose(goal_pose).
  • pick_object(object_id).
  • place_object(object_id, target_pose).
  • follow_person(target_id).

Low-Level Control​

Responsibilities:

  • Translate desired motions into motor commands.
  • Close feedback loops using joint states, IMU, force/torque, etc.
  • Guarantee:
    • Stability (no falls).
    • Safety (respect joint limits and forces).

This layer is largely independent of how tasks are defined; it just executes trajectories safely.

1.4 Feedback Loops and Continuous Reevaluation​

Real environments are dynamic:

  • People move.
  • Objects get bumped.
  • Doors might be closed or opened unexpectedly.

An autonomous agent must:

  • Continuously re-evaluate its assumptions based on new sensor data.
  • Detect when plans are invalidated:
    • Path blocked by a new obstacle.
    • Target object missing from expected location.
  • Trigger replanning or alternative strategies:
    • Choose a different path.
    • Search another room.
    • Ask the user for clarification.

Design implications:

  • Planning should not be a one-shot calculation—use receding horizon or continuous replanning.
  • Behavior trees and task graphs should be designed with:
    • Timeouts.
    • Retry counts.
    • Fallback behaviors.

1.5 From Perception to Agency​

Chapters 2–4 provided:

  • ROS 2 communication and control.
  • A digital twin for safe experimentation.
  • Perception and mapping pipelines.

Chapter 5 adds:

  • Mechanisms for choosing what to do given that information.
  • Structures for sequencing and monitoring complex tasks.
  • Interfaces that tie natural language to physical behavior.

Keep this mental model:

  • Chapter 3: "Where am I and what can I simulate safely?"
  • Chapter 4: "What do I see and how do I represent it?"
  • Chapter 5: "Given what I see and what I want, what should I do next, and how?"

The remaining topics in this chapter turn this conceptual architecture into concrete navigation, task execution, and agentic control pipelines.