arXiv AI
Risk-Aware Semantic Grounding for Trustworthy LLM-Based Robot Planning
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Jun 29
Towards Reliable and Robust LLM Planning: Symbolic Feedback-Driven Iterative Self-Refinement Framework
arXiv:2606. 27757v1 Announce Type: new Abstract: Large language models (LLMs) have attracted widespread attention from academia and industry, yet their deployment raises critical security concerns regarding robustness and reliability.
arXiv AI
Aug 11
Agentic Harnesses: LLM-Driven Verification Layers for Robot Autonomy
arXiv:2608. 09857v1 Announce Type: cross Abstract: Advances in advanced artificial intelligence tools have sparked research in robot autonomy, but the development of such systems has largely focused on execution rather than verifying the feasibility actions planning models propose.
arXiv AI
Sep 16
Bridging Learned Visual Perception and Symbolic Belief-Space Planning
The paper introduces a new paradigm called VLM-as-probabilistic-grounder, which models the uncertainty of Vision‑Language Model (VLM) predicate groundings as a probability distribution over symbolic states. This probabilistic grounding allows belief‑space planning, producing more robust plans in partially observable settings. Experiments in simulated household robot environments demonstrate that this approach improves robustness and task success compared to deterministic grounding methods.
arXiv AI
Aug 28
STEP: State-Aware Task Estimation and Planning with Multi-Modal LLMs for Human-Robot Collaboration
The paper introduces STEP, a State‑Aware Task Estimator and Planner that uses multi‑modal large language models to explicitly estimate system states and predict state transitions during task planning. By forecasting future states alongside actions, STEP reduces hallucinated actions and improves task‑convergent planning. In a simulated robot assembly task, STEP outperforms the state‑of‑the‑art by 32.8% in action executability and 14.8% in final‑state error.
arXiv AI
Sep 18
GAVEL: Graph World Models for Verified and Efficient Long-Horizon LLM Task Planning
GAVEL is a framework that uses an explicit graph world model to verify and repair long‑horizon plans generated by large language models (LLMs). The graph encodes object relations, action pre‑conditions and effects, and probabilistic beliefs about unobserved object locations, allowing the system to predict action outcomes, detect violations, and repair them before execution. In experiments on BEHAVIOR‑1K, GAVEL boosts single‑task success from 41.2 % to 91.8 % and multi‑task success from 19.9 % to 92.6 %, while also reducing travel distance by about 5.4 % compared with a static variant.
arXiv AI
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Actively Resolving Contextual Uncertainty for Underspecified Tasks in Natural Language
The paper introduces CLUE, a framework that lets robots actively resolve contextual uncertainty for underspecified natural language tasks. CLUE employs an LLM-derived policy to generate task-relevant hypotheses and plans, then uses an online language-embedded map to ground these into actions, refining its plan through closed-loop interaction. Experiments on a Boston Dynamics Spot across diverse indoor and outdoor settings show CLUE achieving near-oracle performance and outperforming LLM planners without closed-loop feedback by a significant margin.