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Physical AI & Humanoid Robotics

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Topic 5 — Embodied Action: Manipulation, Interaction & Capstone Milestone

The final ingredient of autonomy is embodied action: the ability to grasp, place, hand over objects, and interact safely with humans. This topic focuses on manipulation, precise control, and the capstone milestone where your humanoid executes a full autonomous task from natural language.

5.1 Grasping and Precision Control​

Manipulation requires accurate:

  • Reach (end-effector position and orientation).
  • Grip (force and contact).
  • Feedback (sensing success or failure).

Key components:

  • Inverse Kinematics (IK):
    • Maps desired end-effector pose to joint angles.
    • Must respect joint limits and collision constraints.
  • Grip-force regulation:
    • Use force/torque sensors or motor current feedback.
    • Avoid crushing delicate objects or dropping heavy ones.
  • Slippage detection:
    • Detect when an object starts to slip.
    • Adjust grip force or re-grasp.

Practical tips:

  • Start with simple, forgiving objects:
    • Boxes, cylinders with good friction.
  • Constrain grasps to:
    • Top grasps or side grasps with clear approach vectors.
  • Use conservative speeds and forces until confidence is built.

5.2 Object Placement & Delivery​

Once your robot can pick up objects, it must place and deliver them reliably.

Steps:

  1. Approach alignment:
    • Position the robot base near the target area.
    • Align the arm so the approach vector is perpendicular or appropriately angled to the surface.
  2. Placement motion:
    • Lower the object gradually.
    • Monitor forces to avoid collisions or pushing other objects.
  3. Release:
    • Open gripper smoothly.
    • Withdraw arm along a safe retreat trajectory.

Considerations:

  • Ensure the target surface is:
    • Within reachable workspace.
    • Free of obstacles.
  • Update world model after placement:
    • New object pose.
    • Freeing of previous location.

5.3 Human Interaction Tasks​

Humanoids are often deployed in shared spaces with humans.

Examples of interaction tasks:

  • Hand-over item:
    • Extend object toward human at a comfortable height and distance.
    • Wait for human to grasp before releasing.
    • Monitor forces to detect successful transfer.
  • Escort person to target room:
    • Use perception to localize the person.
    • Navigate while maintaining safe distance and line-of-sight.
  • Carry objects with compliance:
    • Adjust arm stiffness to tolerate small bumps or guidance from humans.

Design priorities:

  • Safety:
    • Conservative speeds near humans.
    • Clear safety stops and emergency behaviors.
  • Legibility:
    • Motions that are easy for humans to interpret (smooth, predictable).
  • Comfort:
    • Maintain personal space boundaries where possible.

5.4 Capstone Milestone — Full Autonomous Task Demo​

This milestone brings together all previous chapters into a single demonstration.

Scenario​

The humanoid:

  • Receives a natural language task such as:
    • "Bring me the red mug from the kitchen."
    • "Pick up the toolbox from the workbench and deliver it to the storage room."
  • Plans a route through the environment.
  • Finds and grasps the target object.
  • Delivers it to the specified location or person.
  • Operates without manual joystick control or step-by-step teleoperation.

System Components​

  • Perception (Chapter 4):
    • Object detection and pose estimation.
    • SLAM-based mapping and localization.
    • Optional VLM for scene understanding.
  • Navigation (Chapter 5, Topic 2):
    • Waypoint or goal-based navigation with global/local planners.
    • Dynamic obstacle avoidance.
  • Task Execution (Topic 3):
    • Behavior tree or task graph for pick-and-deliver.
    • Skill library for navigation, pick, place.
  • LLM-Based Reasoning (Topic 4):
    • Natural language → structured plan.
    • Clarification and self-correction.
  • Manipulation & Interaction (Topic 5):
    • Grasping and placing.
    • Optional human hand-over.

Objectives​

  • Demonstrate:
    • End-to-end autonomy from language to action.
    • Robustness to minor variations (object slightly moved, starting pose changed).
    • Basic failure recovery (e.g., re-scan or replan).

5.5 Deliverables and Evaluation​

Deliverables​

  • Codebase:
    • ROS 2 packages for perception, navigation, task execution, and LLM integration.
    • Behavior tree or task-graph definitions.
  • Simulation Demo:
    • Recorded runs in the digital twin environment.
    • Logs (rosbags) capturing the full stack in operation.
  • Optional Hardware Demo:
    • Short video of the real robot executing at least one full task.
  • Report:
    • System architecture diagram.
    • Description of skills and tasks.
    • Analysis of success rates and failure modes.

Evaluation Criteria​

  • Autonomy:
    • Does the robot operate with minimal human intervention during tasks?
  • Robustness:
    • How often does the system recover from minor issues without manual resets?
  • Safety & Behavior Quality:
    • Does navigation avoid collisions?
    • Are manipulation and human interactions careful and predictable?
  • Clarity of Design:
    • Are interfaces between modules (perception, planning, control, language) well-defined?
  • Reflection:
    • Does the report clearly identify limitations and future improvement paths?

Reaching this milestone marks the transition from building components to orchestrating a complete physical AI system. Your humanoid is now an agent: it perceives, decides, and acts in the real (or realistically simulated) world.