🧱 Blocksworld Simulation

A visual simulation environment for the classic Blocksworld AI planning problem. The simulation features a robot arm that can manipulate colored blocks across multiple stacks, with support for both interactive GUI control and programmatic access via a REST API.

✨ Features

• Interactive GUI: Pygame-based visual simulation with real-time block manipulation
• REST API: Complete Flask-based API for programmatic control and automation
• LLM-Ready: Compatible with AI assistants through our MCP Server
• Predefined Scenarios: 50 built-in challenges across 5 categories with varying difficulty levels
• Plan Execution & Verification: Test AI-generated plans before execution
• Constraint Sets: Multiple rule sets including standard blocksworld, size-based constraints, and partial observability
• Keyboard Control: Quick manual testing and experimentation

📊 Benchmark Leaderboard

The following table shows benchmark results for different approaches on the given blocksworld scenarios.

Metric	Category	Single Agent (OpenAI o3)	Approach 2	Approach 3
Success Rate	C1	80%
	C2	70%
	C3	100%
	C4	70%
	C5	60%
	total	76%
Avg. Time [s]	C1	75.70
	C2	290.00
	C3	124.90
	C4	731.50
	C5	676.30
	total	379.68
Avg. Attempts	C1	1.10
	C2	1.70
	C3	1.80
	C4	2.20
	C5	3.10
	total	1.98
Avg. Token	C1	35,126
	C2	111,721
	C3	18,195
	C4	143,714
	C5	192,245
	total	58,832
Avg. Steps vs. Optimum	C1	1.00
	C2	1.20
	C3	1.00
	C4	1.08
	C5	0.90
	total	1.03
Avg. Execution Tool Errors	C1	0.00
	C2	0.00
	C3	0.00
	C4	0.00
	C5	0.50
	total	0.16

🚀 Quick Start

Installation

The project uses Poetry for dependency management.

1. Clone the repository:

   git clone <repository-url>cd blocksworld-simulation

2. Install dependencies:

   poetry install

3. Run the simulation:

   poetry run blocksworld-simulation

The GUI will open, and the REST API will be available at http://127.0.0.1:5001.

🤖 LLM Integration

Want to use this simulation with your own AI agents or just try it out with LLMs like Claude or ChatGPT? Check out our MCP Server for Blocksworld Simulation that exposes the simulation as MCP tools.

The MCP server allows LLMs to:

• Interact with the simulation through natural language
• Execute block manipulation actions as tool calls
• Query the simulation state and rules
• Verify and execute multi-step plans

Perfect for AI planning research, testing LLM reasoning capabilities, or building AI agents!

🎮 Control Methods

Keyboard Control

• Start random simulation: Press SPACE (when no simulation is running)
• Pick up/Unstack: Press the letter of the block you want to pick up
• Put down: Press SPACE to place the held block on the ground
• Stack: While holding a block, press the letter of the target block to stack on top of it
• Stop simulation: Press ESC to stop the current simulation and start a new one

REST API Control

The API provides 14 endpoints for complete programmatic control:

Simulation Control

• POST /start_simulation - Start with a scenario or custom configuration
• POST /stop_simulation - Stop the current simulation
• POST /quit - Quit the application

Block Actions

• POST /pick_up - Pick up a block from the ground
• POST /put_down - Put down a held block
• POST /stack - Stack one block on another
• POST /unstack - Unstack one block from another

Plan Execution

• POST /execute_plan - Execute a sequence of actions with GUI animation
• POST /verify_plan - Verify a plan without executing it

Information

• GET /get_status - Get current simulation state (respects partial observability)
• GET /get_full_status - Get complete simulation state (bypasses partial observability)
• GET /get_rules - Get active constraint rules
• GET /scenarios - List all available scenarios
• GET /scenarios/<name_or_id> - Get details for a specific scenario

For detailed API documentation with request/response examples, see the REST API Documentation.

📋 Example Workflow

# 1. Get a scenario with a detailed description
curl http://127.0.0.1:5001/scenarios/2_2
# 2. Start the scenario
curl -X POST http://127.0.0.1:5001/start_simulation \
-H "Content-Type: application/json" \
-d '{"scenario_id": "2_2"}'# 3. Verify your plan before executing
curl -X POST http://127.0.0.1:5001/verify_plan \
-H "Content-Type: application/json" \
-d '{ "plan": [ {"action": "unstack", "block1": "B", "block2": "A"}, {"action": "stack", "block1": "B", "block2": "D"} ] }'# 4. Execute the plan (watch it in the GUI!)
curl -X POST http://127.0.0.1:5001/execute_plan \
-H "Content-Type: application/json" \
-d '{ "plan": [ {"action": "unstack", "block1": "B", "block2": "A"}, {"action": "stack", "block1": "B", "block2": "D"} ] }'

🎯 Constraint Sets

The simulation supports different rule sets:

• base (default): Standard blocksworld with limited ground positions
• block_size: Blocks with varying sizes - blocks can only be placed on larger or equal-sized blocks
• partial_observability: Limited visibility of simulation state

Specify the constraint set when starting a simulation:

curl -X POST http://127.0.0.1:5001/start_simulation \
-H "Content-Type: application/json" \
-d '{"initial_stacks": [["A"], ["B"], ["C"]], "constraint_set": "block_size"}'

📚 Documentation

• REST API Documentation - Complete API reference with examples
• MCP Server Repository - LLM integration via Model Context Protocol
• Scenario definitions: src/blocksworld_simulation/scenarios/definitions/

🔗 Related Projects

• Blocksworld MCP Server - Enable LLMs to control the simulation through MCP tools