TACOS: Task Agnostic COordinator of a multi-drone System

• URL: http://arxiv.org/abs/2510.01869v1
• Date: Thu, 02 Oct 2025 10:21:35 GMT
• Title: TACOS: Task Agnostic COordinator of a multi-drone System
• Authors: Alessandro Nazzari, Roberto Rubinacci, Marco Lovera,
• Abstract summary: TACOS (Task-Agnostic COordinator of a multi-drone System) is a unified framework that enables high-level natural language control of multi-UAV systems.<n>It integrates three key capabilities into a single architecture: a one-to-many natural language interface for intuitive user interaction, an intelligent coordinator for translating user intent into structured task plans, and an autonomous agent that executes plans interacting with the real-world.
• Score: 41.99844472131922
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: When a single pilot is responsible for managing a multi-drone system, the task demands varying levels of autonomy, from direct control of individual UAVs, to group-level coordination, to fully autonomous swarm behaviors for accomplishing high-level tasks. Enabling such flexible interaction requires a framework that supports multiple modes of shared autonomy. As language models continue to improve in reasoning and planning, they provide a natural foundation for such systems, reducing pilot workload by enabling high-level task delegation through intuitive, language-based interfaces. In this paper we present TACOS (Task-Agnostic COordinator of a multi-drone System), a unified framework that enables high-level natural language control of multi-UAV systems through Large Language Models (LLMs). TACOS integrates three key capabilities into a single architecture: a one-to-many natural language interface for intuitive user interaction, an intelligent coordinator for translating user intent into structured task plans, and an autonomous agent that executes plans interacting with the real-world. TACOS allows a LLM to interact with a library of executable APIs, bridging semantic reasoning with real-time multi-robot coordination. We demonstrate the system in real-world multi-drone system and conduct an ablation study to assess the contribution of each module.

Related papers

• HELP: Hierarchical Embodied Language Planner for Household Tasks [75.38606213726906]
  Embodied agents tasked with complex scenarios rely heavily on robust planning capabilities.<n>Large language models equipped with extensive linguistic knowledge can play this role.<n>We propose a Hierarchical Embodied Language Planner, called HELP, consisting of a set of LLM-based agents.
  arXiv  Detail & Related papers  (2025-12-25T15:54:08Z)
• Chat with UAV -- Human-UAV Interaction Based on Large Language Models [6.041434126017702]
  The future of UAV interaction systems is evolving from engineer-driven to user-driven.<n>This paper proposes a novel dual-agent Human-UAV Interaction framework.
  arXiv  Detail & Related papers  (2025-12-09T00:55:40Z)
• Magentic-UI: Towards Human-in-the-loop Agentic Systems [34.50407835250159]
  We introduce Magentic-UI, an open-source web interface for developing and studying human-agent interaction.<n>Built on a flexible multi-agent architecture, Magentic-UI supports web browsing, code execution, and file manipulation.<n>We evaluate Magentic-UI across four dimensions: autonomous task completion on agentic benchmarks, simulated user testing of its interaction capabilities, qualitative studies with real users, and targeted safety assessments.
  arXiv  Detail & Related papers  (2025-07-30T03:49:14Z)
• ICCO: Learning an Instruction-conditioned Coordinator for Language-guided Task-aligned Multi-robot Control [7.335799770583488]
  We propose Instruction-Conditioned Coordinator (ICCO) to enhance coordination in language-guided multi-robot systems.<n>ICCO consists of a Coordinator agent and multiple Local Agents, where the Coordinator generates Task-Aligned and Consistent Instructions.<n>A Consistency Enhancement Term is added to the learning objective to maximize mutual information between instructions and robot behaviors.
  arXiv  Detail & Related papers  (2025-03-15T13:03:20Z)
• Autonomous Deep Agent [0.7489814067742621]
  Deep Agent is an advanced autonomous AI system designed to manage complex multi-phase tasks.<n>The system's foundation is built on our Hierarchical Task DAG framework.<n>Deep Agent establishes a novel paradigm in self-governing AI systems.
  arXiv  Detail & Related papers  (2025-02-10T21:46:54Z)
• ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning [74.58666091522198]
  We present a framework for intuitive robot programming by non-experts. We leverage natural language prompts and contextual information from the Robot Operating System (ROS) Our system integrates large language models (LLMs), enabling non-experts to articulate task requirements to the system through a chat interface.
  arXiv  Detail & Related papers  (2024-06-28T08:28:38Z)
• Unified Human-Scene Interaction via Prompted Chain-of-Contacts [61.87652569413429]
  Human-Scene Interaction (HSI) is a vital component of fields like embodied AI and virtual reality. This paper presents a unified HSI framework, UniHSI, which supports unified control of diverse interactions through language commands.
  arXiv  Detail & Related papers  (2023-09-14T17:59:49Z)
• Planning-oriented Autonomous Driving [60.93767791255728]
  We argue that a favorable framework should be devised and optimized in pursuit of the ultimate goal, i.e., planning of the self-driving car. We introduce Unified Autonomous Driving (UniAD), a comprehensive framework that incorporates full-stack driving tasks in one network.
  arXiv  Detail & Related papers  (2022-12-20T10:47:53Z)
• Autonomous Open-Ended Learning of Tasks with Non-Stationary Interdependencies [64.0476282000118]
  Intrinsic motivations have proven to generate a task-agnostic signal to properly allocate the training time amongst goals. While the majority of works in the field of intrinsically motivated open-ended learning focus on scenarios where goals are independent from each other, only few of them studied the autonomous acquisition of interdependent tasks. In particular, we first deepen the analysis of a previous system, showing the importance of incorporating information about the relationships between tasks at a higher level of the architecture. Then we introduce H-GRAIL, a new system that extends the previous one by adding a new learning layer to store the autonomously acquired sequences
  arXiv  Detail & Related papers  (2022-05-16T10:43:01Z)
• DLGNet-Task: An End-to-end Neural Network Framework for Modeling Multi-turn Multi-domain Task-Oriented Dialogue [0.0]
  We present a new framework, DLGNet-Task, a unified task-oriented dialogue system. Our framework enjoys the controllable, verifiable, and explainable outputs of modular approaches. Using DLGNet-Task in conversational AI systems reduces the level of effort required for developing, deploying, and maintaining intelligent assistants.
  arXiv  Detail & Related papers  (2020-10-04T21:43:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.