Topic 1: Foundations of Autonomy & Agent-Based Robotics

Topic 1 defines what makes a robot autonomous and introduces the basic architecture of an agent that can sense, think, and act without continuous human teleoperation.

1.1 What Makes a Robot Autonomous?

An autonomous robot is more than a remote-controlled tool. It has:

• State awareness: It knows (approximately) where it is, what it is doing, and what the environment looks like.
• Perception: It processes sensor data into a usable world model (from Chapter 4).
• Planning: It decides what to do next to achieve goals.
• Execution: It carries out actions through controllers and actuators.

Key properties of autonomy:

• Independence from teleoperation: The robot can make decisions without a human manually specifying every motion or button press.
• Responsiveness to change: It can react to dynamic environments (moving humans, new obstacles).
• Goal-directed behavior: It optimizes for objectives (reaching a room, delivering an object, following a person) rather than just executing fixed scripts.

You can think of autonomy as a closed-loop decision system:

Sense → Interpret → Decide → Act → Sense again …

This agent loop repeats continuously while the robot is active.

1.2 Architecture of an Autonomous Agent

Many modern autonomous systems share a layered architecture:

• High-level reasoning (LLM / planner):
  • Interprets tasks and goals (often from natural language).
  • Chooses which skills or behaviors to invoke.
• Task planner / behavior controller:
  • Represents tasks as graphs or behavior trees.
  • Sequences skills, handles branching, retries, and termination.
• Skill and motion controllers:
  • Implement concrete capabilities: navigate, pick, place, follow.
  • Map abstract goals (e.g., “go to room B”) to motor commands.
• Feedback loop:
  • Monitors sensor data, task status, and error conditions.
  • Adjusts behavior when things go wrong (obstacle, failure to grasp, etc.).

A simplified view:

User Goal / NL Command
        ↓
  High-Level Reasoning (LLM, planner)
        ↓
   Task Graph / Behavior Tree
        ↓
      Skills (navigate, pick, place, follow)
        ↓
 Controllers → Motors / Actuators
        ↑
     Sensors & State (camera, LiDAR, IMU, joint states)

Throughout Chapter 5, you will flesh out each of these layers and connect them to the perception and control stacks you built in earlier chapters.