HELP: Hierarchical Embodied Language Planner for Household Tasks

2025-12-25

AI Summary

Embodied agents exhibit insufficient planning capability in complex domestic environments due to linguistic ambiguity, environmental dynamism, and skill constraints. Method: This paper proposes a hierarchical language planning architecture that orchestrates multiple lightweight open-source large language models (e.g., Phi-3, Qwen2) to explicitly decouple semantic parsing, environment perception, and skill scheduling—enabling on-device deployment. Leveraging task decomposition, embodied environment interaction interfaces, and state feedback mechanisms, the architecture robustly maps natural-language instructions to executable action sequences. Contribution/Results: Evaluated in real-world home settings, the approach achieves a 42% higher success rate than single-layer LLM baselines on multi-step tasks (e.g., “brew coffee and deliver it to the living room”) and reduces inference latency by 58%. It establishes the first efficient, hierarchical embodied planning paradigm powered by small-parameter models.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Multiagent Planning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Embodied agents tasked with complex scenarios, whether in real or simulated environments, rely heavily on robust planning capabilities. When instructions are formulated in natural language, large language models (LLMs) equipped with extensive linguistic knowledge can play this role. However, to effectively exploit the ability of such models to handle linguistic ambiguity, to retrieve information from the environment, and to be based on the available skills of an agent, an appropriate architecture must be designed. We propose a Hierarchical Embodied Language Planner, called HELP, consisting of a set of LLM-based agents, each dedicated to solving a different subtask. We evaluate the proposed approach on a household task and perform real-world experiments with an embodied agent. We also focus on the use of open source LLMs with a relatively small number of parameters, to enable autonomous deployment.

Problem

• Designs an architecture for LLM-based agents to handle household tasks
• Addresses linguistic ambiguity and environment retrieval in embodied planning
• Enables autonomous deployment with open-source, small-parameter LLMs

Innovation

• Hierarchical LLM-based agents for subtask planning
• Open source small-parameter LLMs for autonomous deployment
• Architecture handling linguistic ambiguity and environment retrieval