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4-3-cognitive-planning-with-llms

Cognitive Planning with LLMs​

The previous section demonstrated how Large Language Models (LLMs) can be used to extract intent and break down high-level commands into a structured sequence of tasks. This section dives deeper into Cognitive Planning with LLMs, focusing on how these abstract task breakdowns are translated into concrete, executable ROS 2 actions for a robot, and the critical importance of safety layers.

Translating Commands into ROS 2 Actions​

For a robot to execute an LLM-generated plan, each high-level action in the plan (e.g., navigate_to_location, pick_up_object) must correspond to a low-level capability the robot possesses. In ROS 2, these capabilities are often exposed through Action Servers.

An Action Server provides a goal-based interface for long-running, cancellable robot tasks. For example:

  • A NavigateToPose action server would handle the robot moving to a specific (x, y, theta) location.
  • A GraspObject action server would manage the sequence of movements required to approach, grasp, and lift an object.
  • A DetectObject action server might handle waiting for a specific object to appear in the camera feed.

The role of the cognitive planner is to map the LLM's output (e.g., the JSON task graph from the previous section) to the available ROS 2 Action Servers. This typically involves:

  1. Parsing LLM Output: Extracting the action type and its parameters from the LLM's structured response.
  2. Action Goal Construction: Creating an instance of the appropriate ROS 2 action goal message type (e.g., NavigateToPose.Goal, GraspObject.Goal) and populating its fields with the extracted parameters.
  3. Action Client Interaction: Using a ROS 2 Action Client to send the goal to the corresponding Action Server. The client can then monitor the progress of the action and receive results or feedback.

Example: "Pick up the cup" → Planning Steps​

Let's break down the command "Pick up the cup" into a sequence of ROS 2 actions, assuming a pre-trained LLM provides the high-level plan and an object detection system (like Isaac ROS DetectNet) can locate the cup.

High-Level Goal: Pick up the cup.

LLM-generated Task Breakdown (Conceptual):

[
  {"action": "find_object", "object_type": "cup"},
  {"action": "navigate_to_object", "object_type": "cup"},
  {"action": "grasp_object", "object_type": "cup"},
  {"action": "lift_object"}
]

Translation to ROS 2 Actions (Python pseudocode):

import rclpy
from rclpy.action import ActionClient
from my_robot_interfaces.action import FindObject, NavigateToPose, GraspObject, LiftObject # Custom ROS 2 Actions

class CognitivePlanner:
    def __init__(self):
        self.node = rclpy.create_node('cognitive_planner')
        self.find_object_client = ActionClient(self.node, FindObject, 'find_object_action')
        self.navigate_client = ActionClient(self.node, NavigateToPose, 'navigate_to_pose_action')
        self.grasp_client = ActionClient(self.node, GraspObject, 'grasp_object_action')
        self.lift_client = ActionClient(self.node, LiftObject, 'lift_object_action')
        # ... other clients

    async def execute_llm_plan(self, llm_plan_json):
        for step in llm_plan_json:
            action_type = step["action"]
            params = step.get("parameters", {})

            if action_type == "find_object":
                goal = FindObject.Goal()
                goal.object_type = params["object_type"]
                # Send goal and await result (object_pose)
                # If found, proceed, else handle failure
                self.node.get_logger().info(f"Finding {goal.object_type}...")
                await self.find_object_client.wait_for_server()
                future = self.find_object_client.send_goal_async(goal)
                response = await future.result()
                if response.accepted:
                    self.node.get_logger().info(f"Found {goal.object_type} at {response.result.object_pose}")
                    object_pose_to_grasp = response.result.object_pose # Store for next step
                else:
                    self.node.get_logger().error(f"Failed to find {goal.object_type}")
                    return False # Plan failed
            elif action_type == "navigate_to_object":
                # Assuming object_pose_to_grasp is available
                goal = NavigateToPose.Goal()
                goal.pose = object_pose_to_grasp # Use detected pose
                # Send goal and await result
                self.node.get_logger().info(f"Navigating to object...")
                await self.navigate_client.wait_for_server()
                future = self.navigate_client.send_goal_async(goal)
                response = await future.result()
                if not response.accepted:
                    self.node.get_logger().error(f"Failed to navigate")
                    return False
            # ... similar logic for grasp_object and lift_object
        return True # Plan executed successfully

# if __name__ == '__main__':
#     rclpy.init()
#     planner = CognitivePlanner()
#     # Example LLM output
#     llm_plan = [{"action": "find_object", "object_type": "cup"}, 
#                 {"action": "navigate_to_object"}, 
#                 {"action": "grasp_object"}, 
#                 {"action": "lift_object"}]
#     rclpy.spin_until_future_complete(planner.node, planner.execute_llm_plan(llm_plan))
#     rclpy.shutdown()

Safety Layers & Verification​

Directly translating LLM outputs into robot actions without careful consideration is extremely risky. LLMs can "hallucinate," generate unsafe commands, or misinterpret context. Therefore, robust safety layers and verification mechanisms are paramount.

Key strategies include:

  • Semantic Parsing & Validation:
    • Schema Enforcement: Ensure the LLM always outputs commands in a predefined structured format (e.g., JSON schema). Reject any output that doesn't conform.
    • Parameter Validation: Verify that the parameters for each action are valid (e.g., a "speed" parameter is within safe limits, a "location" is reachable).
  • Feasibility Checks:
    • Before executing an action, check if it's physically possible and safe given the robot's current state and environment. Can the robot reach that object? Is the path clear?
    • This involves querying the robot's perception system and internal state.
  • Human-in-the-Loop:
    • For critical or ambiguous commands, prompt the human user for confirmation or clarification. "Did you mean the red cup on the left or the right?"
    • Allow for an "emergency stop" or "override" mechanism at any point.
  • Guardrails & Constraints:
    • Hard-code safety limits (e.g., maximum joint velocities, force limits).
    • Provide the LLM with "negative examples" or explicit instructions on what not to do.
    • Use techniques like Reinforcement Learning from Human Feedback (RLHF) to fine-tune the LLM to prioritize safety.

By carefully designing these verification steps, we can harness the powerful cognitive abilities of LLMs while ensuring our humanoid robots operate safely and reliably in the real world.