26 papers · ranked by Valyu relevance
Gupta, Rohan, Asbery, Trevor + 6 more
—Coordinating heterogeneous robot fleets to achieve multiple goals is challenging in multi-robot systems. We introduce an open-source and extensible framework for centralized multirobot task planning and scheduling that leverages LLMs to enable fleets of heterogeneous robots to accomplish multiple tasks. RobotFleet…
Junho Lee, Seabin Lee, Wonjong Lee, Nayoung Kim + 2 more
Large Language Models (LLMs) can reason over complex instructions but often fail to satisfy the physical and spatial constraints required for robotic task planning. Recent LLM-based planners directly translate text into action sequences, yet they lack structured reasoning about feasibility, reachability, and logical…
Mariano Sigman, Stanislas Dehaene, Manfred Fahle
Why is the human brain fundamentally limited when attempting to execute two tasks at the same time or in close succession? Two classical paradigms, psychological refractory period (PRP) and task switching, have independently approached this issue, making significant advances in our understanding of the architecture of…
Dongdong Lu, Mingjie Zhang, Yibo Guo, Hang Li + 1 more
Synthetic Aperture Radar (SAR) image interpretation in dynamic scenarios faces critical challenges, including sluggish multi-agent scheduling responses, sub-optimal task-resource matching, and low full-pipeline collaborative efficiency. To address these issues, this paper proposes an autonomous SAR image interpretation…
Yunfan Li, Bingbing Xu, Xueyun Tian, Xiucheng Xu + 1 more
Recent advances in large language models (LLMs) have enabled agents to autonomously execute complex, long-horizon tasks, yet planning remains a primary bottleneck for reliable task execution. Existing methods typically fall into two paradigms: step-wise planning, which is reactive but often short-sighted; and one-shot…
Meenakshi Amulya Jayanti, X. Y. Han
The Model Context Protocol (MCP) (MCP Community, 2025) has emerged as a widely used framework for enabling LLM-based agents to communicate with external tools and services. The original MCP implementation (Anthropic, 2024) relies on a Large Language Model (LLM) to decompose tasks and issue instructions to servers. In…
Jiyoun Moon, Heoncheol Lee, Shinkyu Park, Seunghwan Lee
As the roles of robots continue to expand in general, there is an increasing demand for research on automated task planning for a multi-agent system that can independently execute tasks in a wide and dynamic environment. This study introduces a plugin framework in which multiple robots can be involved in task planning…
Ruikai Liu, Guangxi Wan, Maowei Jiang, Haojie Chen + 2 more
'Ming Xie'] The Agile Robotics for Industrial Automation Competition (ARIAC) was established to advance flexible manufacturing, aiming to increase the agility of robotic assembly systems in unstructured and dynamic industrial environments. ARIAC 2023 introduced eight agility challenges involving faulty parts, flipped…
Francesco Donnarumma, Thomas Parr, Karl Friston, James Whittington + 1 more
How the brain plans and maintains sequences of future actions remains a central question in systems neuroscience. Recent studies in the frontal cortex have revealed that multiple elements of a sequence are represented simultaneously in separable neural subspaces, challenging classical serial models of sequential…
Carlos G. Correa, Mark K. Ho, Frederick Callaway, Nathaniel D. Daw + 1 more
'Thomas L. Griffiths'] Human behavior emerges from planning over elaborate decompositions of tasks into goals, subgoals, and low-level actions. How are these decompositions created and used? Here, we propose and evaluate a normative framework for task decomposition based on the simple idea that people decompose tasks…
Giacomo Ariani, Neda Kordjazi, J. Andrew Pruszynski, Jörn Diedrichsen
'Jörn Diedrichsen'] Title: Abstract When performing a long chain of actions in rapid sequence, future movements need to be planned concurrently with ongoing action. However, how far ahead we plan, and whether this ability improves with practice, is currently unknown. Here, we designed an experiment in which healthy…
Barbara Arbanas, Tamara Petrović, Matko Orsag, José Ramiro Martínez‐de Dios + 1 more
'José Ramiro Martínez‐de Dios' 'Stjepan Bogdan'] To enable safe and efficient use of multi-robot systems in everyday life, a robust and fast method for coordinating their actions must be developed. In this paper, we present a distributed task allocation and scheduling algorithm for missions where the tasks of different…
Carlos G. Correa, Mark K. Ho, Frederick Callaway, Nathaniel D. Daw + 2 more
'Thomas L. Griffiths' 'Tobias U. Hauser'] Human behavior emerges from planning over elaborate decompositions of tasks into goals, subgoals, and low-level actions. How are these decompositions created and used? Here, we propose and evaluate a normative framework for task decomposition based on the simple idea that…
Marcos Domic-Siede, Martín Irani, Joaquín Valdés, Marcela Perrone-Bertolotti + 1 more
Neural correlates of cognitive planning are not understood well at present. Behavioral paradigms targeting this function are a current challenge in cognitive neuroscience. We recorded EEG activity while subjects were performing a novel behavioral paradigm that evaluates cognitive planning function. Participants showed…
Hannah R. Sheahan, David W. Franklin, Daniel M. Wolpert
Title: Summary Recent theories of limb control emphasize motor cortex as a dynamical system, with planning setting the initial neural state, and execution arising from the self-limiting evolution of the intrinsic neural dynamics. Therefore, movements that share an initial trajectory but then diverge might have…
Ricardo J. Alejandro, Iris Ikink, Emmanouela Foinikianaki, Clay B. Holroyd
Mental planning is essential for producing action sequences. Despite the contribution of planning to many everyday activities, the neurocognitive processes that map a selected plan into a concrete course of action are unclear. We asked whether planning and execution are linked by abstract task relationships that are…
Rick den Otter, Anna Dame, Sjoerd Stuit, Leendert van Maanen
Theories of dual-task interference assume that the same cognitive operations underlie multitasking regardless of stimulus timing, yet this core assumption has remained untested due to methodological limitations of behavioral averaging. Here, we combine hidden multivariate pattern (HMP) analysis with deep spatiotemporal…
Stefano Diomedi, Francesco Edoardo Vaccari, Kostas Hadjidimitrakis, Patrizia Fattori + 1 more
The posterior parietal cortex (PPC) plays a central role in sensorimotor control, performing visuomotor transformations, supporting planning, and providing visual feedback. However, it is unknown how the neural populations in different PPC areas organize their activity during this process. It has been proposed that PPC…
Yuxuan Li, James L. McClelland
When we choose actions aimed at achieving long-range goals, proximal information cannot be exploited in a blindly myopic way, as relevant future information must often be taken into account. However, when long-range information is irrelevant to achieving proximal subgoals, it can be desirable to focus exclusively on…
Samuele Sandrini, Marco Faroni, Nicola Pedrocchi
—A good estimation of the actions' cost is key in task planning for human-robot collaboration. The duration of an action depends on agents' capabilities and the correlation between actions performed simultaneously by the human and the robot. This paper proposes an approach to learning actions' costs and coupling…
Ting Sophia Xu, Lawrence Jun Zhang
Based on Kellogg’s writing model, Skehan’s Limited Attentional Capacity Model (LACM), and Robinson’s Cognition Hypothesis, our study investigated the effects of cognitive task complexity on syntactic complexity, lexical complexity, accuracy, fluency, and functional adequacy in Chinese L2 students’ argumentative…
Peter Kraus, Edan Bainglass, Francisco F. Ramirez, Enea Svaluto-Ferro + 7 more
Compliance with good research data management practices means trust in the integrity of the data, and it is achievable by a full control of the data gathering process. In this work, we demonstrate tooling which bridges these two aspects, and illustrate its use in a case study of automated battery cycling. We…
Authors not listed
Computer-aided synthesis planning aims to identify viable synthetic routes from a target compound to readily available building blocks by iteratively decomposing molecules into smaller precursors. Self-play search algorithms, trained with simulated experience, reach state-of-the-art performance. However, these methods…
Authors not listed
We present RetroSynFormer, a novel approach to multi-step retrosynthesis planning. Here, we express the task of iteratively breaking down a compound into building blocks as a sequence-modeling problem and train a model based on the Decision Transformer. The synthesis routes are generated by iteratively predicting…
Sara Sims, Pinar Demirayak, Simone Cedotal, Kristina Visscher
Central and peripheral vision are important for distinct aspects of everyday life. We use central vision to read and peripheral vision to get the gist of a scene. To understand how these differences are reflected in connectivity between V1 and higher-order cognitive areas, we examined the differential connectivity of…
Grzegorz Skoraczyński, Mateusz Kitlas, Błażej Miasojedow, Anna Gambin
Modern computer-assisted synthesis planning tools provide strong support for this problem. However, they are still limited by computational complexity. This limitation may be overcome by scoring the synthetic accessibility as a pre-retrosynthesis heuristic. A wide range of machine learning scoring approaches is…