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Ngoc La

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2 papers

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HAXP Workshop 2025 Workshop Paper

A Collaborative Numeric Task Planning Framework based on Constraint Translations using LLMs

Anthony Favier
Ngoc La
Pulkit Verma
Julie Shah

Automated planning systems require formal constraint specifications that create significant barriers for domain experts not familiar with those formal specifications, thereby limiting the practical adoption of powerful planning tools in collaborative planning settings. To overcome this challenge, we propose an LLM-based pipeline to translate human natural language constraints into formal hard-trajectory constraints. The initial user input is first refined and decomposed into more explicit natural language constraints, both preparing constraints for formal encoding and offering a chance for the human to review and correct any misinterpretation. Then, the decomposed constraints are encoded into PDDL3. By integrating this with an automated planner, a graphical interface, and PDSim, we created a closed loop where the human gets plan simulations as feedback to their natural language constraints. This innovative collaborative planning framework enables users to leverage their intuition and expertise to intuitively guide automated planning without time-consuming programming expert interventions. Through an ablation study, we demonstrate how our approach significantly improves the syntax and semantic accuracy of the translations compared to direct LLM translations. Our results demonstrate the potential of collaborative planning without technical expert interventions for higher-quality automated solving. On the other hand, our negative results seem to highlight the limitations of using PDDL3 constraints to leverage human high-level guidance as we expected, raising interesting reflections and potential discussions.

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PRL Workshop 2025 Workshop Paper

HDDLGym: A Tool for Studying Multi-Agent Hierarchical Problems Defined in HDDL with OpenAI Gym

Ngoc La
Ruaridh Mon-Williams

In recent years, reinforcement learning (RL) methods have been extensively tested using tools like OpenAI Gym, even though many tasks in these environments could also benefit from hierarchical planning. However, there is currently no tool that facilitates the seamless integration of hierarchical planning with RL. Hierarchical Domain Definition Language (HDDL), used in classical planning, introduces a structured approach well-suited for model-based RL to address this gap. To facilitate this integration, we introduce HDDLGym, a Python-based tool that automatically generates OpenAI Gym environments from HDDL domains and problems. HDDLGym bridges RL and hierarchical planning, supporting multi-agent scenarios and enabling collaborative planning among agents. This paper provides an overview of HDDLGym’s design and implementation, highlighting the challenges and design choices involved in integrating HDDL with the Gym interface and applying RL policies to hierarchical planning. We also provide detailed instructions and demonstrations for using the HDDLGym framework, including how to work with existing HDDL domains and problems from International Planning Competitions, exemplified by the Transport domain. Additionally, we offer guidance on creating new HDDL domains for multi-agent scenarios and demonstrate the practical use of HDDLGym in the Overcooked domain. By leveraging the advantages of HDDL and Gym, HDDLGym aims to be a valuable tool for studying RL in hierarchical planning, particularly in multi-agent contexts. Code — https: //github. com/HDDLGym/HDDLGym

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