Coordinating Task Switching in a Robotics Multi-Agent System Using Behavior Trees
- URL: http://arxiv.org/abs/2606.01170v1
- Date: Sun, 31 May 2026 11:22:16 GMT
- Title: Coordinating Task Switching in a Robotics Multi-Agent System Using Behavior Trees
- Abstract summary: We present a Behavior-Tree-based approach to support multi-robot coordination within the VSSS team of the ThundeRatz robotics team from the Universidade de S$tildea$o Paulo.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The application of multi-agent systems in robotics is a very challenging field. Several competitions involving such systems are proposed to foster research and development of strategies and mechanisms using games as the underlying domain. Among them are the ones from the \textit{IEEE Very Small Soccer (VSSS)} category, which is the case study described in this paper. In VSSS, two teams of three robots each compete in a very dynamic environment of a soccer game. Thus, coordination of robots' behavior during the game is crucial to win it. In this paper, we present a Behavior-Tree-based approach to support multi-robot coordination within the VSSS team of the ThundeRatz robotics team from the Universidade de S$\tilde{a}$o Paulo. Moreover, a comparison between the proposed approach and the previous one, which was based on a Finite State Machine (FSM), was conducted using the FIRASim simulator. Besides that, the performance of this new strategy was further evaluated in an academic robotics competition.
Related papers
- Seeing Together: Multi-Robot Cooperative Egocentric Spatial Reasoning with Multimodal Large Language Models [76.29781698910202]
We propose a framework for fine-grained cooperative spatial reasoning.<n> SP-CoR combines dynamics-aware multi-robot frame sampling, spectral- and physics-guided view fusion, and physics-aligned prompt distillation.<n>It consistently improves cooperative reasoning, outperforming the strongest finetuned baseline by +3.87% on Habitat and +7.12% on iGibson.
arXiv Detail & Related papers (2026-05-18T14:04:26Z) - Cooperative-Competitive Team Play of Real-World Craft Robots [21.285783193272987]
We develop a comprehensive robotic system, including simulation, distributed learning framework, and physical robot components.<n>We then propose and evaluate reinforcement learning techniques designed for efficient training of cooperative and competitive policies on this platform.<n>We demonstrate the effectiveness of our approach through experiments with a multi-robot car competitive game and a cooperative task in real-world settings.
arXiv Detail & Related papers (2026-02-24T17:15:37Z) - Heterogeneous Robot Collaboration in Unstructured Environments with Grounded Generative Intelligence [54.91177026001217]
Large language model (LLM)-enabled teaming methods typically assume well-structured and known environments.<n>We present SPINE-HT, a framework that addresses these limitations by grounding the reasoning abilities of LLMs in the context of a heterogeneous robot team.<n>Our framework achieves nearly twice the success rate compared to prior LLM-enabled heterogeneous teaming approaches.
arXiv Detail & Related papers (2025-10-30T18:24:38Z) - Benchmarking Generalizable Bimanual Manipulation: RoboTwin Dual-Arm Collaboration Challenge at CVPR 2025 MEIS Workshop [120.2806035123366]
RoboTwin Dual-Arm Collaboration Challenge was held at the 2nd MEIS Workshop, CVPR 2025.<n>Competitors totally tackled 17 dual-arm manipulation tasks, covering rigid, deformable, and tactile-based scenarios.<n>Report outlines the competition setup, task design, evaluation methodology, key findings and future direction.
arXiv Detail & Related papers (2025-06-29T17:56:41Z) - Toward Real-World Cooperative and Competitive Soccer with Quadrupedal Robot Teams [18.20205072720915]
We present a hierarchical multi-agent reinforcement learning (MARL) framework that enables fully autonomous and decentralized quadruped robot soccer.<n>First, a set of highly dynamic low-level skills is trained for legged locomotion and ball manipulation, such as walking, dribbling, and kicking.<n>On top of these, a high-level strategic planning policy is trained with Multi-Agent Proximal Policy Optimization (MAPPO) via Fictitious Self-Play.
arXiv Detail & Related papers (2025-05-20T02:20:54Z) - EMOS: Embodiment-aware Heterogeneous Multi-robot Operating System with LLM Agents [33.77674812074215]
We introduce a novel multi-agent framework designed to enable effective collaboration among heterogeneous robots.<n>We propose a self-prompted approach, where agents comprehend robot URDF files and call robot kinematics tools to generate descriptions of their physics capabilities.<n>The Habitat-MAS benchmark is designed to assess how a multi-agent framework handles tasks that require embodiment-aware reasoning.
arXiv Detail & Related papers (2024-10-30T03:20:01Z) - GRUtopia: Dream General Robots in a City at Scale [65.08318324604116]
This paper introduces project GRUtopia, the first simulated interactive 3D society designed for various robots.
GRScenes includes 100k interactive, finely annotated scenes, which can be freely combined into city-scale environments.
GRResidents is a Large Language Model (LLM) driven Non-Player Character (NPC) system that is responsible for social interaction.
arXiv Detail & Related papers (2024-07-15T17:40:46Z) - Commonsense Reasoning for Legged Robot Adaptation with Vision-Language Models [81.55156507635286]
Legged robots are physically capable of navigating a diverse variety of environments and overcoming a wide range of obstructions.
Current learning methods often struggle with generalization to the long tail of unexpected situations without heavy human supervision.
We propose a system, VLM-Predictive Control (VLM-PC), combining two key components that we find to be crucial for eliciting on-the-fly, adaptive behavior selection.
arXiv Detail & Related papers (2024-07-02T21:00:30Z) - Play Everywhere: A Temporal Logic based Game Environment Independent Approach for Playing Soccer with Robots [2.3999111269325266]
We present a hierarchical representation of soccer in which the robot selects the level of operation based on the perceived semantic characteristics of the environment.
The proposed approach enables the robot to operate in unstructured environments, just as it happens when humans go from soccer played on an official field to soccer played on a street.
arXiv Detail & Related papers (2024-05-21T09:30:47Z) - Interactive Multi-Robot Flocking with Gesture Responsiveness and Musical Accompaniment [0.7659052547635159]
This work presents a compelling multi-robot task in which the main aim is to enthrall and interest.
In this task, the goal is for a human to be drawn to move alongside and participate in a dynamic, expressive robot flock.
Towards this aim, the research team created algorithms for robot movements and engaging interaction modes such as gestures and sound.
arXiv Detail & Related papers (2024-03-30T18:16:28Z) - 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)
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.