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.
In partially observable settings, agents must act without full knowledge of the world state and rely on uncertain state-estimation pipelines. Obtaining grounded and verifiable symbolic plans under suc...
arXiv:2505.13180v3 Announce Type: replace Abstract: Integrating Large Language Models with symbolic planners is a promising direction for obtaining verifiable and grounded plans, with recent works ex...
arXiv:2606. 15654v1 Announce Type: cross Abstract: Real-world robot task planning must operate under both stochastic action execution and partial observability, yet constructing Partially Observable Markov Decision Process (POMDP) models for real robotics domains remains difficult and labor-intensive.
arXiv:2607. 08024v1 Announce Type: cross Abstract: Long-horizon robot planning requires jointly reasoning over semantic task structure and geometric feasibility.
HINT-Plan is a new method that integrates human intention prediction into robot task planning by using Vision Language Models to infer high‑level human intentions from third‑person images. These intentions are converted into goal states and combined with hierarchical Scene Graphs to formulate joint task‑planning problems in context‑rich environments. In a photorealistic simulation, HINT-Plan achieved a 69.71% success rate, outperforming baselines by up to 35.29% and reducing functional conflicts.
Language and vision-language models generate plausible embodied plans but do not guarantee executability, as their outputs can violate environment dynamics or act on incorrectly grounded entities. We present a neurosymbolic agent that factors long-horizon household tasks into task-directed visual exploration and constrained symbolic planning.
Embodied planning increasingly relies on vision-language models (VLMs) to translate instructions and visual observations into executable action sequences. However, fluent plans are not always executab...
arXiv:2609.37554v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as high-level planners in robot navigation, but their outputs may become unreliable when instructi...
arXiv:2608. 20084v1 Announce Type: cross Abstract: Robots executing long-horizon manipulation tasks from natural-language instructions must reason about both semantic task structure and geometric feasibility.
arXiv:2608. 16794v1 Announce Type: cross Abstract: Language and vision-language models generate plausible embodied plans but do not guarantee executability, as their outputs can violate environment dynamics or act on incorrectly grounded entities.
arXiv:2512. 19178v2 Announce Type: replace-cross Abstract: Bridging the gap between natural language commands and autonomous execution in unstructured environments remains an open challenge for robotics.
arXiv:2609.08602v1 Announce Type: new Abstract: Embodied planning increasingly relies on vision-language models (VLMs) to translate instructions and visual observations into executable action sequenc...