RobotFleet: An Open-Source Framework for Centralized Multi-Robot Task Planning
- URL: http://arxiv.org/abs/2510.10379v1
- Date: Sun, 12 Oct 2025 00:32:37 GMT
- Title: RobotFleet: An Open-Source Framework for Centralized Multi-Robot Task Planning
- Abstract summary: We introduce an open-source framework for central multi-robot task planning and scheduling.<n>RobotFleet provides abstractions for planning, scheduling, and execution across robots deployed as containerized services.
- Score: 10.128950558854347
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Coordinating heterogeneous robot fleets to achieve multiple goals is challenging in multi-robot systems. We introduce an open-source and extensible framework for centralized multi-robot task planning and scheduling that leverages LLMs to enable fleets of heterogeneous robots to accomplish multiple tasks. RobotFleet provides abstractions for planning, scheduling, and execution across robots deployed as containerized services to simplify fleet scaling and management. The framework maintains a shared declarative world state and two-way communication for task execution and replanning. By modularizing each layer of the autonomy stack and using LLMs for open-world reasoning, RobotFleet lowers the barrier to building scalable multi-robot systems. The code can be found here: https://github.com/therohangupta/robot-fleet.
Related papers
- D-VLC: Decentralized Vision-Language Collaboration for Heterogeneous Embodied Multi-Robot Systems in Unknown Environments [10.393866368123946]
Multi-robot systems can improve the efficiency of complex task execution through parallel collaboration and complementary capabilities.<n>We propose a framework that combines decentralized asynchronous reasoning, lightweight information sharing, capability aware collaboration, and a unified action interface.<n>Experiments across diverse scenarios and multiple VLMs show success rates above 70%, with completion time reduced by up to 55.8% relative to the geometric greedy baseline.
arXiv Detail & Related papers (2026-07-31T04:20:32Z) - RACAS: Controlling Diverse Robots With a Single Agentic System [26.695876900159373]
We introduce RACAS (Robot-Agnostic Control via Agentic Systems), a cooperative agentic architecture in which three modules communicate exclusively through natural language to provide closed-loop robot control.<n> RACAS requires only a natural language description of the robot, a definition of available actions, and a task specification; no source code, model weights, or reward functions need to be modified to move between platforms.<n>We evaluate RACAS on several tasks using a wheeled ground robot, a recently published novel multi-jointed robotic limb, and an underwater vehicle. RACAS consistently solved all assigned tasks across these radically different platforms, demonstrating the potential
arXiv Detail & Related papers (2026-03-05T19:25:01Z) - LEO-RobotAgent: A General-purpose Robotic Agent for Language-driven Embodied Operator [1.5566524830295307]
We propose LEO-RobotAgent, a general-purpose language-driven intelligent agent framework for robots.<n>This framework features strong generalization, robustness, and efficiency.<n>Experiments have verified that this framework can be easily adapted to mainstream robot platforms.
arXiv Detail & Related papers (2025-12-11T12:58:36Z) - Distilling On-device Language Models for Robot Planning with Minimal Human Intervention [116.93160528413655]
PRISM is a framework for distilling small language model (SLM)-enabled robot planners.<n>We apply PRISM to three LLM-enabled planners for mapping and exploration, manipulation, and household assistance.<n>We demonstrate that PRISM improves the performance of Llama-3.2-3B from 10-20% of GPT-4o's performance to over 93%.
arXiv Detail & Related papers (2025-06-20T21:44:27Z) - Deploying Foundation Model-Enabled Air and Ground Robots in the Field: Challenges and Opportunities [65.98704516122228]
The integration of foundation models (FMs) into robotics has enabled robots to understand natural language and reason about the semantics in their environments.<n>This paper addresses the deployment of FM-enabled robots in the field, where missions often require a robot to operate in large-scale and unstructured environments.<n>We present the first demonstration of large-scale LLM-enabled robot planning in unstructured environments with several kilometers of missions.
arXiv Detail & Related papers (2025-05-14T15:28:43Z) - RoboMatrix: A Skill-centric Hierarchical Framework for Scalable Robot Task Planning and Execution in Open-World [18.44952696124717]
RoboMatrix is a skill-centric hierarchical framework designed for scalable robot task planning and execution in open-world environments.<n>Key innovation of our work is the introduction of the first unified vision-language-action (VLA) model capable of seamlessly integrating both movement and manipulation within one model.
arXiv Detail & Related papers (2024-11-29T17:36:03Z) - COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large Language Models [49.24666980374751]
COHERENT is a novel LLM-based task planning framework for collaboration of heterogeneous multi-robot systems.<n>A Proposal-Execution-Feedback-Adjustment mechanism is designed to decompose and assign actions for individual robots.<n>The experimental results show that our work surpasses the previous methods by a large margin in terms of success rate and execution efficiency.
arXiv Detail & Related papers (2024-09-23T15:53:41Z) - 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) - RoboScript: Code Generation for Free-Form Manipulation Tasks across Real
and Simulation [77.41969287400977]
This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation.
We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language.
We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
arXiv Detail & Related papers (2024-02-22T15:12:00Z) - SkiROS2: A skill-based Robot Control Platform for ROS [1.4502611532302039]
We introduce SkiROS2, a skill-based robot control platform on top of ROS.
SkiROS2 proposes a layered, hybrid control structure for automated task planning, and reactive execution.
We relate SkiROS2 to the field and outline three example use cases that cover task planning, reasoning, multisensory input, integration in a manufacturing execution system and reinforcement learning.
arXiv Detail & Related papers (2023-06-29T15:25:51Z) - Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human
Supervision [72.4735163268491]
Commercial and industrial deployments of robot fleets often fall back on remote human teleoperators during execution.
We formalize the Interactive Fleet Learning (IFL) setting, in which multiple robots interactively query and learn from multiple human supervisors.
We propose Fleet-DAgger, a family of IFL algorithms, and compare a novel Fleet-DAgger algorithm to 4 baselines in simulation.
arXiv Detail & Related papers (2022-06-29T01:23:57Z)
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.