Bridging Probabilistic Inference and Behavior Trees: An Interactive Framework for Adaptive Multi-Robot Cooperation

• URL: http://arxiv.org/abs/2512.04404v1
• Date: Thu, 04 Dec 2025 03:08:55 GMT
• Title: Bridging Probabilistic Inference and Behavior Trees: An Interactive Framework for Adaptive Multi-Robot Cooperation
• Abstract summary: This paper proposes an Interactive Inference Behavior Tree (IIBT) framework that integrates behavior trees (BTs) with active inference.<n>The proposed IIBT node extends conventional BTs with probabilistic reasoning, enabling online joint planning and execution across multiple robots.<n> Experimental results demonstrate that the IIBT framework reduces BT node complexity by over 70%, while maintaining robust, interpretable, and adaptive cooperative behavior.
• Score: 8.358857123007006
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes an Interactive Inference Behavior Tree (IIBT) framework that integrates behavior trees (BTs) with active inference under the free energy principle for distributed multi-robot decision-making. The proposed IIBT node extends conventional BTs with probabilistic reasoning, enabling online joint planning and execution across multiple robots. It remains fully compatible with standard BT architectures, allowing seamless integration into existing multi-robot control systems. Within this framework, multi-robot cooperation is formulated as a free-energy minimization process, where each robot dynamically updates its preference matrix based on perceptual inputs and peer intentions, thereby achieving adaptive coordination in partially observable and dynamic environments. The proposed approach is validated through both simulation and real-world experiments, including a multi-robot maze navigation and a collaborative manipulation task, compared against traditional BTs(https://youtu.be/KX_oT3IDTf4). Experimental results demonstrate that the IIBT framework reduces BT node complexity by over 70%, while maintaining robust, interpretable, and adaptive cooperative behavior under environmental uncertainty.

Related papers

• HRIBench: Benchmarking Interaction-Centric Human-Robot Collaboration [62.19372189311434]
  We introduce HRIBench, a diagnostic benchmark for intent-aware human-robot collaboration based on executable scenarios.<n>HRIBench represents collaborative tasks as structured scenario scripts that explicitly model agent roles, temporal dependencies, coordination constraints, and human behavior distributions.<n>Results show that current foundation robot policies struggle substantially in collaborative settings despite strong manipulation ability.
  arXiv  Detail & Related papers  (2026-07-05T09:15:35Z)
• Higher Order Reasoning for Collaborative Communicationless Mobile Robot Operations [4.239775815863115]
  In communicationless environments, multi-robot systems must operate without the constant information exchange that many coordination strategies typically assume.<n>This paper presents a novel dynamic epistemic planning framework that enables implicit coordination and long horizon planning through higher-order reasoning among robots.
  arXiv  Detail & Related papers  (2026-05-21T02:22:09Z)
• Robotic Grasping and Placement Controlled by EEG-Based Hybrid Visual and Motor Imagery [64.82869118243723]
  We present a framework that integrates EEG-based visual and motor imagery (VI/MI) with robotic control to enable real-time, intention-driven grasping and placement.<n>Motivated by the promise of BCI-driven robotics, this system bridges neural signals with physical control by deploying offline-pretrained decoders in a zero-shot manner.
  arXiv  Detail & Related papers  (2026-03-03T17:41:42Z)
• Scalable Multi-Robot Path Planning via Quadratic Unconstrained Binary Optimization [0.0]
  Multi-Agent Path Finding (MAPF) remains a fundamental challenge in robotics.<n>This paper investigates Quadratic Unconstrained Binary Optimization (QUBO) as a structurally scalable alternative for simultaneous multi-robot path planning.
  arXiv  Detail & Related papers  (2026-02-16T14:50:04Z)
• InterAgent: Physics-based Multi-agent Command Execution via Diffusion on Interaction Graphs [72.5651722107621]
  InterAgent is an end-to-end framework for text-driven physics-based multi-agent humanoid control.<n>We introduce an autoregressive diffusion transformer equipped with multi-stream blocks, which decouples proprioception, exteroception, and action to cross-modal interference.<n>We also propose a novel interaction graph exteroception representation that explicitly captures fine-grained joint-to-joint spatial dependencies.
  arXiv  Detail & Related papers  (2025-12-08T10:46:01Z)
• Behavior Trees vs Executable Ontologies: a Comparative Analysis of Robot Control Paradigms [51.56484100374058]
  We compare two approaches to modeling robotic behavior: imperative Behavior Trees (BTs) and declarative Executable Ontologies (EO)<n>BTs structure behavior hierarchically using control-flow, whereas EO represents the domain as a temporal, event-based semantic graph driven by dataflow rules.
  arXiv  Detail & Related papers  (2025-11-19T09:38:01Z)
• MRBTP: Efficient Multi-Robot Behavior Tree Planning and Collaboration [6.239895985962529]
  Multi-robot task planning and collaboration are critical challenges in robotics.<n>We propose the Multi-Robot Behavior Tree Planning (MRBTP) algorithm, with theoretical guarantees of both soundness and completeness.<n>We then propose an optional plugin for MRBTP when Large Language Models (LLMs) are available to reason goal-related actions for each robot.
  arXiv  Detail & Related papers  (2025-02-25T10:39:28Z)
• Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot Coordination [1.5970870882365]
  We propose Capability-Aware Shared Hypernetworks (CASH) for multi-robot teams.<n>CASH is a soft weight sharing architecture that uses hypernetworks to efficiently learn a flexible shared policy.<n>We show that CASH consistently outperforms baseline architectures in terms of performance and sample efficiency during both training and zero-shot generalization.
  arXiv  Detail & Related papers  (2025-01-10T15:39:39Z)
• Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation [50.01551945190676]
  Social robot navigation can be helpful in various contexts of daily life but requires safe human-robot interactions and efficient trajectory planning. We propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures. We demonstrate its effectiveness for multi-agent trajectory prediction and social robot navigation.
  arXiv  Detail & Related papers  (2024-01-22T18:58:22Z)
• Multi-robot Social-aware Cooperative Planning in Pedestrian Environments Using Multi-agent Reinforcement Learning [2.7716102039510564]
  We propose a novel multi-robot social-aware efficient cooperative planner that on the basis of off-policy multi-agent reinforcement learning (MARL) We adopt temporal-spatial graph (TSG)-based social encoder to better extract the importance of social relation between each robot and the pedestrians in its field of view (FOV)
  arXiv  Detail & Related papers  (2022-11-29T03:38:47Z)
• Active Inference and Behavior Trees for Reactive Action Planning and Execution in Robotics [2.040132783511305]
  We propose a hybrid combination of active inference and behavior trees (BTs) for reactive action planning and execution in dynamic environments. The proposed approach allows to handle partially observable initial states and improves the robustness of classical BTs against unexpected contingencies.
  arXiv  Detail & Related papers  (2020-11-19T10:24:41Z)
• Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction [55.569050872780224]
  We present an online framework for safe crowd-robot interaction based on risk-sensitive optimal control, wherein the risk is modeled by the entropic risk measure. Our modular approach decouples the crowd-robot interaction into learning-based prediction and model-based control. A simulation study and a real-world experiment show that the proposed framework can accomplish safe and efficient navigation while avoiding collisions with more than 50 humans in the scene.
  arXiv  Detail & Related papers  (2020-09-12T02:02:52Z)

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.