Hierarchical Planning in Robotics

Let’s consider an example of hierarchical planning applied to a robotic arm tasked with assembling electronic devices:

1. High-Level Goal: ensemble electronic devices following the provided assembly process
2. Major Steps:
   1. Identifying Components: identify and local the components required for assembling the device.
   2. Planning Assembly Sequence: determine optimal sequence of assembly steps to minimize assembly time and maximize efficiency.
   3. Manipulation and Grasping: manipulate the arm of the robot to grasp and manipulate the electronic components.
   4. Quality Control: ensure the quality of assembly step and detect and correct any errors.
3. Minor Steps:
   1. Identifying Components:
      1. Object Recognition: recognize components using computer vision
      2. Inventory Management: maintain inventory of the components
   2. Planning Assembly Sequence:
      1. Task Planning: break the process into smaller and sequential tasks
      2. Motion Planning: planning the arm movement to perform assembling
   3. Manipulation and Grasping:
      1. Grasping strategy: determine optimal grasping pose based on shape and side of the components.
      2. Path Generation: generate smooth trajectories for the robotic arm
   4. Quality Control:
      1. Vision Inspection: inspect assembled components using cameras
      2. Feedback Control: implement feedback mechanism to adjust assembly actions.
4. Hierarchical Planning:
   1. First Level Plan: Define the high-level goals and major steps, including component identification, assembly sequence planning, manipulation and grasping, and quality control.
   2. Second Level Plan: Break down each major step into subtasks and minor steps, such as object recognition, inventory management, task planning, motion planning, grasping strategy, path generation, vision inspection, and feedback control.
   3. Third Level Plan: Further decompose the minor steps into detailed actions and algorithms necessary to execute them effectively, such as specific image processing algorithms for object recognition or control algorithms for adjusting robotic arm movements based on inspection results.

Hierarchical Planning Techniques in Robotics

In robotic arm assembly, hierarchical planning techniques like HTN planning, hierarchical reinforcement learning (HRL), HTNs, and hierarchical state space search ensure efficient execution.

• HTN Planning: Organizes major steps hierarchically, facilitating task decomposition.
• Hierarchical Reinforcement Learning (HRL): Learns hierarchical policies for manipulation and grasping, optimizing performance.
• HTNs (Hierarchical Task Networks): Structures minor steps systematically, ensuring accurate component handling.
• Hierarchical State Space Search: Optimizes assembly sequences considering constraints, ensuring efficiency.

Hierarchical Planning in Artificial Intelligence is a problem-solving and decision-making technique employed to reduce the computational expense associated with planning. The article provides an overview of hierarchical planning in AI, discussing its components, techniques, applications in autonomous driving and robotics, advantages, and challenges.