Grounding Language Models with Semantic Digital Twins for Robotic Planning

• URL: http://arxiv.org/abs/2506.16493v1
• Date: Thu, 19 Jun 2025 17:38:00 GMT
• Title: Grounding Language Models with Semantic Digital Twins for Robotic Planning
• Authors: Mehreen Naeem, Andrew Melnik, Michael Beetz,
• Abstract summary: We introduce a novel framework that integrates Semantic Digital Twins (SDTs) with Large Language Models (LLMs)<n>The proposed framework effectively combines high-level reasoning with semantic environment understanding, achieving reliable task completion in the face of uncertainty and failure.
• Score: 6.474368392218828
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a novel framework that integrates Semantic Digital Twins (SDTs) with Large Language Models (LLMs) to enable adaptive and goal-driven robotic task execution in dynamic environments. The system decomposes natural language instructions into structured action triplets, which are grounded in contextual environmental data provided by the SDT. This semantic grounding allows the robot to interpret object affordances and interaction rules, enabling action planning and real-time adaptability. In case of execution failures, the LLM utilizes error feedback and SDT insights to generate recovery strategies and iteratively revise the action plan. We evaluate our approach using tasks from the ALFRED benchmark, demonstrating robust performance across various household scenarios. The proposed framework effectively combines high-level reasoning with semantic environment understanding, achieving reliable task completion in the face of uncertainty and failure.

Related papers

• Context Matters! Relaxing Goals with LLMs for Feasible 3D Scene Planning [2.111102681327218]
  We present an approach integrating classical planning with Large Language Models.<n>We propose a hierarchical formulation that enables robots to make unfeasible tasks tractable.<n>Our method demonstrates its ability to adapt and execute tasks effectively within environments modeled using 3D Scene Graphs.
  arXiv  Detail & Related papers  (2025-06-18T19:14:56Z)
• Ontology-driven Prompt Tuning for LLM-based Task and Motion Planning [0.20940572815908076]
  Task and Motion Planning (TAMP) approaches combine high-level symbolic plan with low-level motion planning.<n>LLMs are transforming task planning by offering natural language as an intuitive and flexible way to describe tasks.<n>This work proposes a novel prompt-tuning framework that employs knowledge-based reasoning to refine and expand user prompts.
  arXiv  Detail & Related papers  (2024-12-10T13:18:45Z)
• Interactive and Expressive Code-Augmented Planning with Large Language Models [62.799579304821826]
  Large Language Models (LLMs) demonstrate strong abilities in common-sense reasoning and interactive decision-making. Recent techniques have sought to structure LLM outputs using control flow and other code-adjacent techniques to improve planning performance. We propose REPL-Plan, an LLM planning approach that is fully code-expressive and dynamic.
  arXiv  Detail & Related papers  (2024-11-21T04:23:17Z)
• Flex: End-to-End Text-Instructed Visual Navigation from Foundation Model Features [59.892436892964376]
  We investigate the minimal data requirements and architectural adaptations necessary to achieve robust closed-loop performance with vision-based control policies.<n>Our findings are synthesized in Flex (Fly lexically), a framework that uses pre-trained Vision Language Models (VLMs) as frozen patch-wise feature extractors.<n>We demonstrate the effectiveness of this approach on a quadrotor fly-to-target task, where agents trained via behavior cloning successfully generalize to real-world scenes.
  arXiv  Detail & Related papers  (2024-10-16T19:59:31Z)
• Autonomous Behavior Planning For Humanoid Loco-manipulation Through Grounded Language Model [6.9268843428933025]
  Large language models (LLMs) have demonstrated powerful planning and reasoning capabilities for comprehension and processing of semantic information. We propose a novel language-model based framework that enables robots to autonomously plan behaviors and low-level execution under given textual instructions.
  arXiv  Detail & Related papers  (2024-08-15T17:33:32Z)
• LangSuitE: Planning, Controlling and Interacting with Large Language Models in Embodied Text Environments [70.91258869156353]
  We introduce LangSuitE, a versatile and simulation-free testbed featuring 6 representative embodied tasks in textual embodied worlds. Compared with previous LLM-based testbeds, LangSuitE offers adaptability to diverse environments without multiple simulation engines. We devise a novel chain-of-thought (CoT) schema, EmMem, which summarizes embodied states w.r.t. history information.
  arXiv  Detail & Related papers  (2024-06-24T03:36:29Z)
• Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning [79.38140606606126]
  We propose an algorithmic framework that fine-tunes vision-language models (VLMs) with reinforcement learning (RL) Our framework provides a task description and then prompts the VLM to generate chain-of-thought (CoT) reasoning. We demonstrate that our proposed framework enhances the decision-making capabilities of VLM agents across various tasks.
  arXiv  Detail & Related papers  (2024-05-16T17:50:19Z)
• Temporal and Semantic Evaluation Metrics for Foundation Models in Post-Hoc Analysis of Robotic Sub-tasks [1.8124328823188356]
  We present an automated framework to decompose trajectory data into temporally bounded and natural language-based descriptive sub-tasks.<n>Our framework provides both time-based and language-based descriptions for lower-level sub-tasks that comprise full trajectories.<n>The metrics measure the temporal alignment and semantic fidelity of language descriptions between two sub-task decompositions.
  arXiv  Detail & Related papers  (2024-03-25T22:39:20Z)
• Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks [54.60571399091711]
  Large Language Models (LLMs) have achieved impressive results in creating robotic agents for performing open vocabulary tasks. We present an interactive planning technique for partially observable tasks using LLMs.
  arXiv  Detail & Related papers  (2023-12-11T22:54:44Z)
• AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers [20.857692296678632]
  For effective human-robot interaction, robots need to understand, plan, and execute complex, long-horizon tasks. Recent advances in large language models have shown promise for translating natural language into robot action sequences. We show that our approach outperforms several methods using LLMs as planners in complex task domains.
  arXiv  Detail & Related papers  (2023-06-10T21:58:29Z)
• ProgPrompt: Generating Situated Robot Task Plans using Large Language Models [68.57918965060787]
  Large language models (LLMs) can be used to score potential next actions during task planning. We present a programmatic LLM prompt structure that enables plan generation functional across situated environments.
  arXiv  Detail & Related papers  (2022-09-22T20:29:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.