SPAR is a framework that leverages the generative capabilities of LLMs to automatically produce valid, diverse, and semantically accurate PDDL domains from natural language input.

Authors: Songhao Huang *, Yuwei Wu *, Guangyao Shi, Gaurav S. Sukhatme, and Vijay Kumar

Related Paper: Songhao Huang*, Yuwei Wu*, Guangyao Shi, Gaurav S. Sukhatme, and Vijay Kumar. "SPAR: Scalable LLM-based PDDL Domain Generation for Aerial Robotics." arxiv Preprint

If this repo helps your research, please cite our paper at:

@article{huang2025spar,
  title={SPAR: Scalable LLM-based PDDL Domain Generation for Aerial Robotics},
  author={Huang, Songhao and Wu, Yuwei and Shi, Guangyao and Sukhatme, Gaurav S and Kumar, Vijay},
  journal={arXiv preprint arXiv:2509.13691},
  year={2025}
}

• action_gen.py: generate a PDDL domain for one benchmark domain.
• eval_syntax.py: run domain-generation experiments and aggregate syntax error counts.
• problem_gen.py: generate PDDL problem files compatible with generated domains.
• batch_solve.py: solve generated domain/problem pairs with ENHSP.
• eval/domain_similarity.py: validate plans against generated domains with VAL.
• pddl_validator.py: syntax and semantic checks used during iterative correction.
• llm_model.py: model wrapper, embedding lookup, and retrieval utilities.
• planner/: ENHSP wrapper plus the bundled enhsp-20.jar.
• prompts/: prompt templates, retrieval assets, and embedding-cache scripts.
• uav_domain_benchmark/: benchmark and dataset for UAV task-planning domains.

• Python 3.10+
• Java 17+ available as java, or set JAVA_BIN
• At least one model API key:
  • OPENAI_API_KEY for OpenAI models
  • DEEPSEEK_API_KEY for DeepSeek models
• Local sentence-transformer checkpoint for retrieval:
  • local_model/all-mpnet-base-v2

Optional:

• VAL_BIN for eval/domain_similarity.py
• local_model/bge-reranker-v2-m3 for regenerating BGE retrieval caches

Install dependencies:

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Set the environment variables you need:

export OPENAI_API_KEY=your_key_here
export DEEPSEEK_API_KEY=your_key_here
export JAVA_BIN=java
export VAL_BIN=/path/to/Validate

Edit the variables in the __main__ block of action_gen.py:

• _domain_name_str
• _engine
• _prompt_method
• _result_log_dir

Then run:

python action_gen.py

Outputs include the generated domain, intermediate LLM transcripts, extracted predicates/functions, and validation error counts.

eval_syntax.py has two entry paths:

• syntax_eval() to generate domains and log validation errors
• total_error_count() to aggregate existing results

Before running, edit the module-level settings in eval_syntax.py, especially:

• engine_list
• prompt_method_restart_list
• restart_domain
• restart_method
• restart_engine

Then run:

python eval_syntax.py

Results are written under results/<timestamp>/<engine>/<domain>/<prompt_method>/.

problem_gen.py expects generated domains to already exist under results/. Update the module-level variables near the top of the file:

• date_str
• engine
• gpt_engine
• restart controls such as restart_domain, restart_method, and restart_problem

Then run:

python problem_gen.py

Generated problems are written under:

results/<date_str>/<engine>/<domain>/<prompt_method>/pddl/

Edit the module-level configuration in batch_solve.py:

• date_str
• engine
• prompt_method_restart_list
• optional restart filters such as restart_domain

Then run:

python batch_solve.py

The script writes .plan files next to generated problems and prints per-method success rates.

eval/domain_similarity.py compares generated plans and generated domains using VAL.

Requirements:

• source plans in the benchmark domain folders under uav_domain_benchmark/<domain>/pddl/*.plan
• generated domains and problems under results/
• VAL_BIN pointing to the Validate executable

Edit the module-level variables in eval/domain_similarity.py, then run:

python eval/domain_similarity.py

If you need to rebuild the retrieval cache, update the options in prompts/save_action_embed.py and run:

python prompts/save_action_embed.py

This only requires OPENAI_API_KEY when the script is configured with use_llm=True.

• ENHSP: planner-based evaluation of generated domain and problem pairs.
• VAL: plan validation.
• all-mpnet-base-v2: retrieval-based prompting and action similarity search.
• OpenAI API and DeepSeek API

For any technical issues, please contact Yuwei Wu ([email protected], [email protected]).