arXiv:2510. 14828v3 Announce Type: replace Abstract: Improving the reasoning capabilities of embodied agents is crucial for robots to complete complex human instructions in long-view manipulation tasks successfully.
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...
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:2609.08602v1 Announce Type: new Abstract: Embodied planning increasingly relies on vision-language models (VLMs) to translate instructions and visual observations into executable action sequenc...
arXiv:2606. 06877v1 Announce Type: cross Abstract: Task planning often suffers from severe efficiency bottlenecks when robots must reason over long-horizon action sequences under complex logical constraints, including object affordances, spatial relationships, and sequential action dependencies.
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:2608. 20237v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) combine linguistic reasoning with visual perception, yet their ability to perform visual spatial planning under explicit or previously unseen rule constraints remains underexplored.
arXiv:2607. 05377v1 Announce Type: cross Abstract: While recent Vision-Language-Action (VLA) models show promise toward generalist manipulation policies, they struggle with long-horizon tasks due to their Markovian nature-relying solely on current observations.
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.
The paper proposes a neuro‑symbolic framework that augments vision‑language‑action (VLA) models with explicit task graphs and multimodal procedural memory to handle long‑horizon manipulation tasks. Task graphs encode action dependencies, valid transitions, and branch conditions, while memory tracks the active step, completed actions, textual context, and relevant visual evidence. Human demonstrations provide spatial and temporal guidance via gaze or saliency cues, which are annotated in robot‑view teleoperation videos and used to fine‑tune VLA models. The approach is evaluated on workspace clearing and surgical‑instrument handling tasks, measuring object and destination selection, subtask completion, task progress, step‑order consistency, overall success, and procedural or execution mistakes.
arXiv:2607. 14187v1 Announce Type: new Abstract: Embodied cognition requires agents to connect high-level task reasoning with the physical states to be achieved.
arXiv:2607. 15641v1 Announce Type: cross Abstract: Humans combine reasoning and motor control to solve complex manipulation tasks under diverse constraints.