The paper introduces a method for generating legible plans in arbitrary PDDL domains by extending prior legibility research to classical planning without custom planners. It incorporates a second‑order theory of mind to estimate the observer’s perspective, enabling robots to implicitly communicate goals in human‑robot teaming. Benchmark results show that increasing legibility typically trades off with plan efficiency, and a regularizing factor is needed to balance the two.
arXiv:2607. 20289v1 Announce Type: cross Abstract: We consider a task planning scenario in which robots sharing a persistent environment are assigned tasks one at a time from a held-out sequence.
arXiv:2607. 08024v1 Announce Type: cross Abstract: Long-horizon robot planning requires jointly reasoning over semantic task structure and geometric feasibility.
arXiv:2510. 00182v2 Announce Type: replace-cross Abstract: While we know that large language models (LLMs) can solve some planning problems, we do not understand the extent of these capabilities for robotics.
arXiv:2603.08814v2 Announce Type: replace-cross Abstract: Long-horizon task planning for heterogeneous multi-robot systems is essential for deploying collaborative teams in real-world environments; y...
arXiv:2609.05519v1 Announce Type: cross Abstract: We propose a unified strategy for fast goal inference in human-robot interaction. The core idea is to drive the human toward Critical Decision Points...
arXiv:2609.25187v2 Announce Type: replace Abstract: Task planning bridges high-level instructions and executable behavior in long-horizon manipulation, yet modern Vision-Language-Action (VLA) systems...
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.
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.
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:2503. 22122v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) have demonstrated remarkable capabilities in robotic planning, particularly for long-horizon tasks that require a holistic understanding of the environment for task decomposition.
arXiv:2403. 16178v2 Announce Type: replace-cross Abstract: For effective human-agent teaming, robots and other artificial intelligence (AI) agents must infer their human partner's abilities and behavioral response patterns and adapt accordingly.