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Module 6: Walking & Grasping Overview

Welcome to Module 6, the culmination of our journey into Physical AI and Humanoid Robotics. This module tackles two of the most challenging problems in robotics: locomotion (maintaining balance and moving in complex environments) and manipulation (interacting with objects dexterously).

The Hard Problems: Locomotion and Manipulation​

Locomotion: The Challenge of Balance and Movement​

For humans, walking, running, and climbing are largely subconscious acts. For robots, particularly humanoids, achieving stable and efficient locomotion is an immense engineering and algorithmic feat. It involves:

  • Balance Control: Actively maintaining equilibrium against gravity and external forces.
  • Gait Generation: Creating smooth, natural, and energy-efficient walking patterns.
  • Terrain Adaptation: Adjusting movement strategies for uneven, slippery, or deformable surfaces.

Manipulation: Interacting with the World​

Dexterous manipulation—the ability to grasp, lift, place, and reorient objects—is another grand challenge. It requires:

  • Perception: Accurately identifying object properties, poses, and environmental constraints.
  • Grasping: Planning and executing stable grasps for various object shapes and textures.
  • Force Control: Applying appropriate forces to objects without damaging them or dropping them.
  • Task Planning: Decomposing complex manipulation goals into sequences of primitive actions.

This module will explore both classical and modern AI-driven approaches to these problems, demonstrating how we can enable robots to move and interact with the physical world with increasing autonomy and dexterity.

Learning Goals for Weeks 14-15 (Capstone)​

By the end of this module, you will be able to:

  • Understand Kinematics: Differentiate between Forward and Inverse Kinematics and apply them to robot arm control.
  • Utilize Motion Planning: Employ tools like MoveIt 2 for collision-aware path planning for robotic manipulators.
  • Implement Gait Generation: Grasp the principles of stable bipedal locomotion, including Zero Moment Point (ZMP) control.
  • Train RL Policies for Control: Develop and train reinforcement learning policies to achieve complex locomotion and manipulation behaviors without hard-coding rules.

This module brings together all the concepts from previous weeks, completing your understanding of the full hardware and software stack required for advanced Physical AI and humanoid robotics. You will see how perception, simulation, and learning culminate in intelligent physical action.