Planning with LLMs involves using large language models to generate action sequences that achieve specified goals — leveraging LLMs' understanding of tasks, common sense, and procedural knowledge to create plans for robots, agents, and automated systems, bridging natural language goal specifications with executable action sequences.
What Is AI Planning?
- Planning: Finding a sequence of actions that transforms an initial state into a goal state.
- Components:
- Initial State: Current situation.
- Goal: Desired situation.
- Actions: Operations that change state.
- Plan: Sequence of actions achieving the goal.
Why Use LLMs for Planning?
- Natural Language Goals: LLMs can understand goals expressed in natural language — "make breakfast," "clean the room."
- Common Sense: LLMs have learned common-sense knowledge about how the world works.
- Procedural Knowledge: LLMs have seen many examples of plans and procedures in training data.
- Flexibility: LLMs can adapt plans to different contexts and constraints.
How LLMs Generate Plans
1. Goal Understanding: LLM interprets the natural language goal.
2. Plan Generation: LLM generates a sequence of actions.
``
Goal: "Make a cup of coffee"
LLM-generated plan:
1. Fill kettle with water
2. Boil water
3. Put coffee grounds in filter
4. Pour hot water over grounds
5. Wait for brewing to complete
6. Pour coffee into cup
3. Refinement: LLM can refine the plan based on feedback or constraints.
4. Execution: Actions are executed by a robot or system.
LLM Planning Approaches
- Direct Generation: LLM generates complete plan in one shot.
- Fast but may not handle complex constraints.
- Iterative Refinement: LLM generates plan, checks feasibility, refines.
- More robust for complex problems.
- Hierarchical Planning: LLM decomposes goal into subgoals, plans for each.
- Handles complex tasks by breaking them down.
- Reactive Planning: LLM generates next action based on current state.
- Adapts to dynamic environments.
Example: Household Robot Planning
`
Goal: "Set the table for dinner"
LLM-generated plan:
1. Navigate to kitchen
2. Open cabinet
3. Grasp plate
4. Place plate on table
5. Repeat steps 2-4 for additional plates
6. Grasp fork from drawer
7. Place fork next to plate
8. Repeat steps 6-7 for additional forks
9. Grasp knife from drawer
10. Place knife next to plate
11. Repeat steps 9-10 for additional knives
12. Grasp glass from cabinet
13. Place glass on table
14. Repeat steps 12-13 for additional glasses
`
Challenges
- Feasibility: LLM-generated plans may not be physically feasible.
- Example: "Pick up the table" — table may be too heavy.
- Solution: Verify plan with physics simulator or feasibility checker.
- Completeness: Plans may miss necessary steps.
- Example: Forgetting to open door before walking through.
- Solution: Use verification or execution feedback to identify gaps.
- Optimality: Plans may not be optimal — longer or more costly than necessary.
- Solution: Use optimization or search to improve plans.
- Grounding: Mapping high-level actions to low-level robot commands.
- Example: "Grasp cup" → specific motor commands.
- Solution: Use motion planning and control systems.
LLM + Classical Planning
- Hybrid Approach: Combine LLM with classical planners (STRIPS, PDDL).
- LLM: Generates high-level plan structure, handles natural language.
- Classical Planner: Ensures logical correctness, handles constraints.
- Process:
1. LLM translates natural language goal to formal specification (PDDL).
2. Classical planner finds valid plan.
3. LLM translates plan back to natural language or executable actions.
Example: LLM Translating to PDDL
`
Natural Language Goal: "Move all blocks from table A to table B"
LLM-generated PDDL:
(define (problem move-blocks)
(:domain blocks-world)
(:objects
block1 block2 block3 - block
tableA tableB - table)
(:init
(on block1 tableA)
(on block2 tableA)
(on block3 tableA))
(:goal
(and (on block1 tableB)
(on block2 tableB)
(on block3 tableB))))
Classical planner generates valid action sequence.
`
Applications
- Robotics: Plan robot actions for manipulation, navigation, assembly.
- Virtual Assistants: Plan sequences of API calls to accomplish user requests.
- Game AI: Plan NPC behaviors and strategies.
- Workflow Automation: Plan business process steps.
- Smart Homes: Plan device actions to achieve user goals.
LLM Planning with Feedback
- Execution Monitoring: Observe plan execution, detect failures.
- Replanning: If action fails, LLM generates alternative plan.
- Learning: LLM learns from failures to improve future plans.
Example: Replanning
`
Initial Plan: "Pick up cup from table"
Execution: Robot attempts to grasp cup → fails (cup is too slippery)
LLM Replanning:
"Cup is slippery. Alternative plan:
1. Get paper towel
2. Dry cup
3. Pick up cup with better grip"
``
Evaluation
- Success Rate: What percentage of plans achieve the goal?
- Efficiency: How many actions does the plan require?
- Robustness: Does the plan handle unexpected situations?
- Generalization: Does the planner work on novel tasks?
LLMs vs. Classical Planning
- Classical Planning:
- Pros: Guarantees correctness, handles complex constraints, optimal solutions.
- Cons: Requires formal specifications, limited to predefined action spaces.
- LLM Planning:
- Pros: Natural language interface, common sense, flexible, handles novel tasks.
- Cons: No correctness guarantees, may generate infeasible plans.
- Best Practice: Combine both — LLM for high-level reasoning, classical planner for correctness.
Benefits
- Natural Language Interface: Users specify goals in plain language.
- Common Sense: LLMs bring real-world knowledge to planning.
- Flexibility: Adapts to new tasks without reprogramming.
- Rapid Prototyping: Quickly generate plans for testing.
Limitations
- No Guarantees: Plans may be incorrect or infeasible.
- Grounding Gap: High-level plans need translation to low-level actions.
- Context Limits: LLMs have limited context — may not track complex state.
Planning with LLMs is an emerging and promising approach — it makes AI planning more accessible and flexible by leveraging natural language understanding and common sense, though it requires careful integration with verification and execution systems to ensure reliability.