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LLM-Empowered Embodied Agent for Memory-Augmented Task Planning in Household Robotics

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arxiv 2504.21716 v1 pith:WZYNFRJN submitted 2025-04-30 cs.RO cs.AIcs.CL

Marc Glocker , Peter Hönig , Matthias Hirschmanner , Markus Vincze This is my paper
classification cs.ROcs.AIcs.CL
keywords planningtaskagentsystemhouseholdobjectagentsembodied
verification ladder T0 review T1 audit T2 compute T3 formal
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We present an embodied robotic system with an LLM-driven agent-orchestration architecture for autonomous household object management. The system integrates memory-augmented task planning, enabling robots to execute high-level user commands while tracking past actions. It employs three specialized agents: a routing agent, a task planning agent, and a knowledge base agent, each powered by task-specific LLMs. By leveraging in-context learning, our system avoids the need for explicit model training. RAG enables the system to retrieve context from past interactions, enhancing long-term object tracking. A combination of Grounded SAM and LLaMa3.2-Vision provides robust object detection, facilitating semantic scene understanding for task planning. Evaluation across three household scenarios demonstrates high task planning accuracy and an improvement in memory recall due to RAG. Specifically, Qwen2.5 yields best performance for specialized agents, while LLaMA3.1 excels in routing tasks. The source code is available at: https://github.com/marc1198/chat-hsr.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Any House Any Task: Scalable Long-Horizon Planning for Abstract Human Tasks

    cs.RO 2026-02 conditional novelty 6.0 of 10

    A reinforcement-trained LLM that decomposes abstract household requests into PDDL subgoals and solves them with a symbolic planner outperforms prompting and end-to-end planning baselines on long-horizon tasks.

  2. ProbGuard: Proactive Runtime Monitoring for LLM Agent Safety via Probabilistic Prediction

    cs.AI 2025-08 conditional novelty 6.0 of 10

    Pro2Guard learns a discrete-time Markov chain from LLM agent traces and triggers intervention when the computed probability of reaching an unsafe state exceeds a user-set threshold.

  3. Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges

    cs.DC 2025-07 conditional novelty 4.0 of 10

    A survey that builds a taxonomy of edge-cloud LLM-SLM collaboration for inference and training, claiming to be the first to unify both phases.

  4. Foundation Model Driven Robotics: A Comprehensive Review

    cs.RO 2025-07 conditional novelty 2.0 of 10

    A review of foundation-model-driven robotics that synthesizes recent work across perception, planning, control, HRI, simulation, and sim-to-real transfer, and highlights open challenges.

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