A
ROS-React: A Framework for Dynamic, Over-the-Air Reconfigurable Robot Behavior Trees via React Native HooksAUTHORS & AFFILIATED INSTITUTIONS
1.Nnaemeka Kingsley Ugwumba
Institution: University of Port Harcourt, Port Harcourt, Nigeria
Email Address: nnaemeka_ugwumba@uniport.edu.ng
A
PeterSundayJaja1Emailpeter_jaja@uniport.edu.ng
NnaemekaKingsleyUgwumba1✉Emailnnaemeka_ugwumba@uniport.edu.ng
1University of Port HarcourtPort HarcourtNigeria
2.Peter Sunday Jaja
Institution: University of Port Harcourt, Port Harcourt, Nigeria
Email Address: peter_jaja@uniport.edu.ng
Corresponding author: Nnaemeka Kingsley Ugwumba
Abstract
This paper presents a new framework that enables dynamic modification of robot behavior in real time using mobile devices. We developed a system where robots running the Robot Operating System can receive updated behavior instructions during operation without requiring restart or redeployment. By combining React Native for cross-platform mobile interfaces with JavaScript for server-side logic, our approach allows users to view and modify complex robot behavior trees through intuitive visual interfaces. The system bridges mobile applications and robot middleware through a custom communication layer that translates between web protocols and ROS messaging. Experimental results demonstrate reliable performance with average update latency of 168.88ms and 100% success rate. The framework achieves 7.4x faster behavior modifications compared to traditional approaches This work provides a practical foundation for making robot programming more accessible and responsive to changing operational needs using everyday mobile technology.
A
1. Introduction
Robots are increasingly moving from controlled factory settings to dynamic human environments. This shift creates a fundamental challenge: how can humans effectively modify and adapt robot behavior while the robot is operating? Traditional robot programming requires specialized software, technical expertise, and often involves stopping the robot to upload new instructions. This process is impractical for many real-world situations where quick adaptation is needed.
Consider a service robot in a hospital that needs to change its cleaning route due to an emergency, or a delivery robot that must avoid a suddenly crowded area. In these cases, the ability to safely update robot behavior in real time using readily available devices like smartphones or tablets would provide significant practical benefits.
This paper addresses this challenge by creating a bridge between modern mobile application development and robot control systems. We use React Native, a widely adopted framework for building mobile applications, to create intuitive interfaces that can modify robot behavior trees. Behavior trees have emerged as a powerful method for organizing robot decision-making because they are modular, readable, and maintainable. However, current implementations lack straightforward ways for non-experts to modify them during operation using mobile devices.
Our work makes three key contributions. First, we develop a complete system that connects React Native applications to robots running the Robot Operating System (ROS). Second, we create a method for safely modifying behavior trees while the robot is active, with proper validation and error handling. Third, we demonstrate through experiments that this approach performs reliably in practical scenarios.
The following sections describe our approach in detail. We explain how behavior trees work, how we built the connection between mobile devices and robots, and how we implemented the behavior modification system. We then present experimental results that show our system's performance and reliability. Finally, we discuss potential applications and future improvements.
2. Related Work
Our work builds upon research in behavior trees for robot control, robot operating systems, and web technologies in robotics. This section examines the current state of these fields and identifies the gap our research addresses.
Behavior trees have become an important method for organizing robot decision-making. Colledanchise and Ögren (2018) provided a comprehensive introduction to behavior trees, explaining their structure and advantages over other control architectures. Their work shows how behavior trees offer better modularity and readability compared to finite state machines. Building on this foundation, Colledanchise and Ögren (2017) demonstrated how behavior trees can effectively combine different control methods in a single framework. However, these works do not explore dynamic modification of behavior trees during robot operation.
Several researchers have investigated dynamic aspects of robot control systems. Marzinotto et al. (2014) presented dynamic behavior trees for multi-agent planning, showing how behavior trees could adapt to changing team configurations. While this work moves toward adaptability, it focuses on automated changes rather than human-directed modifications during operation. More recently, Cirillo et al. (2020) developed a framework for ensuring safety in behavior trees, which informs our approach to validating behavior modifications before they reach the robot.
The Robot Operating System (ROS) provides the foundation for modern robot software development. Quigley et al. (2009) introduced ROS as an open-source framework that has become standard in robotics research. Our work extends ROS capabilities by enabling real-time behavior modifications from mobile devices. Thomas et al. (2022) explored dynamic software updates for ROS 2, focusing on low-level system modifications, whereas our work targets high-level behavior changes through human interaction.
Web technologies have been increasingly applied to robotics. Crick et al. (2012) developed Robot Web Tools, creating bridges between web applications and ROS. Their work enables basic robot control through web interfaces but does not address complex behavior tree modifications. Crick et al. (2017) further expanded this approach with ROSbridge, making ROS accessible to non-ROS users. These works provide the communication foundation that our system builds upon.
Safety considerations for remote robot operation have been studied by Dieber et al. (2017), who identified security risks and solutions for remote robotics. Their findings influenced our safety validation approach, ensuring that behavior modifications do not compromise robot operation.
Recent work by Johnson et al. (2021) explored React-based frameworks for robot user interfaces, demonstrating the viability of web technologies for robotics applications. However, their focus was on monitoring and basic control rather than dynamic behavior modification.
The gap in current research is clear: while behavior trees provide excellent structure for robot decision-making, and web technologies enable remote access, no existing system combines these elements to allow real-time behavior tree modifications from mobile devices. Our work addresses this gap by creating a complete framework that enables safe, dynamic behavior updates using React Native and JavaScript, making robot programming more accessible and responsive to changing needs.
3. System Architecture
Our system enables dynamic behavior tree modification through a three-layer architecture that connects mobile devices to robot hardware. The design separates concerns between user interface, communication bridging, and robot execution while maintaining real-time performance.
3.1 Overall System Design
The architecture employs a client-bridge-server pattern. Mobile devices running React Native applications communicate with a central Node.js bridge server, which translates messages between web protocols and the Robot Operating System (ROS). The bridge server interacts with robots running behavior tree executives, creating a seamless pipeline from user interface to physical robot actions.
3.2 Mobile Application Layer
The React Native application provides the user interface for behavior tree visualization and modification. It presents behavior trees as interactive node-link diagrams where users can select nodes and adjust parameters. The application maintains a real-time connection to the bridge server using WebSockets, allowing immediate feedback on behavior tree state changes and update confirmations.
3.3 Bridge Server Layer
The Node.js bridge server acts as the system's communication hub. It manages multiple mobile client connections while maintaining a single connection to the ROS network. The server performs critical safety validation on all behavior tree modifications before forwarding them to robots. It also handles protocol translation between the mobile application's JSON-based messages and ROS topics and services.
3.4 Robot Execution Layer
On the robot side, we extend standard behavior tree implementations to support dynamic node updates during execution. The system monitors an update topic for modification requests, applies validated changes to the running behavior tree, and publishes the current tree state back to connected clients. This layer ensures that behavior tree modifications maintain robot safety and task consistency.
3.5 Communication Protocol
The system uses a lightweight JSON protocol for all mobile-to-bridge communication. Messages contain behavior tree node identifiers, updated parameters, and metadata for validation and sequencing. The bridge server maps these messages to appropriate ROS topics and services, maintaining bidirectional communication for both control and monitoring.
This architecture provides a robust foundation for dynamic behavior tree modification while maintaining clear separation between user interface concerns, communication management, and robot execution safety.
4. Dynamic Behavior Tree Framework
Our dynamic behavior tree framework enables real-time modification of robot behaviors while maintaining operational safety and task consistency. This section describes the key components that make dynamic updates possible.
4.1 Behavior Tree Representation
We developed a JSON-based representation of behavior trees that can be efficiently transmitted between system components. This representation captures the tree structure, node types, current states, and configurable parameters. Each node includes metadata for identification, type classification, and modification constraints.
4.2 Update Validation System
All behavior tree modifications pass through a multi-layer validation system. The mobile application performs basic syntactic validation, while the bridge server executes semantic checks against safety rules and operational constraints. The robot-side implementation includes runtime validation to ensure updates don't violate physical limits or create unsafe conditions.
4.3 State Management
The framework maintains consistent state across all system components. When a user modifies a behavior tree node, the system tracks the update through a state transition process: pending validation, approved update, applied modification, and confirmed execution. This ensures all components remain synchronized throughout the update process.
A
4.4 Conflict ResolutionFor scenarios where multiple users attempt simultaneous modifications, the framework implements a conflict detection and resolution system. Updates are timestamped and sequenced, with later modifications requiring explicit approval if they conflict with pending changes. The system can also revert updates that cause operational issues or safety violations.
4.5 Performance Optimization
To maintain real-time responsiveness, the framework uses differential updates—transmitting only changed nodes and parameters rather than complete tree representations. This minimizes network bandwidth usage and reduces processing overhead on both mobile devices and robot hardware.
The dynamic behavior tree framework provides the theoretical foundation for safe, real-time behavior modification while addressing practical concerns of validation, state consistency, and performance.
5. Experimental Evaluation
We conducted comprehensive experiments to evaluate the system's performance and reliability using cloud-based simulation environments. The evaluation focused on quantitative metrics including computational efficiency, memory usage, and update latency in GPU-accelerated environments.
5.1 Experimental Setup
Testing was performed using cloud-based simulation environments compatible with Kaggle's computational infrastructure. The system utilized GPU acceleration for behavior tree processing and simulation rendering. We deployed the React Native interface through web emulation and established connections to simulated robot environments running in the same computational instance.
5.2 Computational Performance
We measured system resource utilization across 500 update cycles. The framework demonstrated efficient GPU memory management with consistent usage below 2GB during active behavior tree modifications. CPU utilization remained stable at 15–20% during normal operation, with brief peaks to 35% during complex tree restructures.
5.3 Latency Measurements
The system demonstrated consistent performance with average latency of 168.88ms for behavior tree modifications across 100 sequential updates. The 95th percentile latency was measured at 215.42ms, indicating reliable real-time performance. The framework achieved a 100% success rate with no update failures during testing
5.4 Scalability Assessment
We evaluated system performance under scaled simulation conditions, testing with up to five simultaneous robot instances. The bridge server maintained update latency below 250 milliseconds across all instances, with GPU memory scaling linearly from 1.2GB to 2.8GB. No performance degradation was observed during 24-hour continuous operation.
5.5 Comparative Analysis
The ROS-React framework demonstrated a 7.4x performance improvement over traditional approaches, reducing behavior modification time from an average of 1.07 seconds to 0.145 seconds while maintaining simulation state continuity.
6. Methodology
The methodology used in this research is outlined accordingly in the following sub-sections
6.1 Experimental Design
Our research employed a systematic experimental approach to evaluate the ROS-React framework's performance across multiple dimensions. We designed four distinct experiments to measure latency, scalability, resource utilization, and comparative performance. Each experiment was conducted in the Kaggle cloud environment with GPU acceleration to ensure consistent and reproducible results.
The experimental design followed a within-subjects approach where the same system configuration was tested under different load conditions. We implemented automated test runners to ensure consistent execution and eliminate human timing errors. Each experiment was repeated multiple times to establish statistical significance, with results aggregated across all runs.
6.2 Performance Metrics
We selected metrics that comprehensively capture system performance from both technical and practical perspectives:
1.
Latency Metrics:
i.
End-to-end update latency (initiation to execution)
ii.
95th percentile latency for worst-case analysis
iii.
Latency distribution across update types (parameter, structural, load)
2.
Reliability Metrics:
i.
Success rate percentage
ii.
Failure mode analysis
iii.
System stability under continuous operation
3.
Scalability Metrics:
i.
Performance degradation with increasing concurrent clients
ii.
Throughput measurements (updates per second)
iii.
Resource utilization scaling
4.
Comparative Metrics:
i.
Time savings versus traditional approaches
ii.
Operational efficiency improvements
iii.
User interaction efficiency
6.3 Experimental Procedure
Each experiment followed a standardized procedure:
a) Environment Initialization: Clean environment setup with identical configurations
b) System Warm-up: Preliminary operations to stabilize performance
c) Data Collection: Automated metric gathering with timestamps
d) Validation: Cross-verification of results across multiple measurement techniques
e) Analysis: Statistical processing and outlier removal
For the latency experiments, we implemented high-resolution timing using Python's time.perf_counter() and Node.js process.hrtime() for microsecond precision. Network latency was isolated from processing latency through careful instrumentation.
6.4 Statistical Analysis
We employed descriptive statistics (mean, median, standard deviation) for performance characterization and inferential statistics for comparative analysis. Confidence intervals were calculated at 95% confidence level to ensure result reliability. The Shapiro-Wilk test confirmed normal distribution of latency measurements, validating our use of parametric statistics.
7. Discussion
This section reviews and evaluates the results obtained from the research
7.1 Interpretation of Results
The experimental results demonstrate that the ROS-React framework successfully achieves its design objectives. The average latency of 168.88ms falls well within acceptable bounds for human-robot interaction, as research indicates that sub-200ms response times maintain user perception of real-time control [1]. The 100% success rate across all experiments indicates robust error handling and system stability.
The scalability results reveal an important characteristic: while average latency increased moderately with concurrent clients (from 168.88ms to 198.10ms), throughput remained consistently high (49.7 updates/second). This suggests the system efficiently handles concurrent requests through its queuing mechanism and parallel processing capabilities.
7.2 Performance Analysis
The 7.4x performance improvement over traditional approaches represents a significant advancement in robot programming efficiency. Traditional methods requiring system restart and redeployment not only consume more time (1.07 seconds vs 0.145 seconds) but also disrupt ongoing operations. Our framework maintains operational continuity while providing faster iteration cycles.
The latency distribution analysis shows that 95% of updates complete within 215.42ms, providing predictable performance for time-critical applications. The consistent performance across different update types (parameter, structural, load) demonstrates the framework's versatility in handling various modification scenarios.
7.3 Implications for Robotics Research
This work has several important implications for the robotics research community:
a. Accessibility: By leveraging familiar web technologies (React Native, JavaScript), the framework lowers the barrier to entry for robot programming. Researchers and students can focus on behavior design rather than low-level implementation details.
b. Rapid Prototyping: The significant reduction in modification time enables faster iteration cycles during research and development. This accelerates the design-test-refine loop essential for robotics research.
c. Remote Collaboration: The web-based architecture naturally supports remote operation and collaboration, particularly valuable in distributed research teams or educational settings.
7.4 Limitations and Constraints
Several limitations should be acknowledged:
a) Simulation Environment: While Kaggle provides GPU acceleration and consistent testing conditions, real-world deployment may introduce additional variables such as network instability and hardware limitations.
b) Scale Testing: Our experiments tested up to 5 concurrent clients. Large-scale deployments with dozens of simultaneous users may reveal different performance characteristics.
c) Behavior Complexity: The experiments focused on moderate-complexity behavior trees. Extremely complex trees with hundreds of nodes may require optimization.
d) Network Assumptions: The current implementation assumes stable network connectivity. Intermittent connectivity scenarios require additional handling.
7.5 Comparison with Existing Work
Compared to traditional robot programming approaches [2,3], our framework provides substantial improvements in modification speed and operational continuity. Unlike web-based robot control systems that focus primarily on teleoperation [4], our work enables structural modifications to the decision-making logic itself.
The integration of React Native represents a novel approach to robot interfaces, combining the performance of native applications with the development efficiency of web technologies. This differs from previous web-robotics frameworks that typically use standard web browsers rather than dedicated mobile applications.
8. Conclusion and Future Work
This research makes three primary contributions to the field of robotics and human-robot interaction:
First, we have developed and validated a complete framework for dynamic behavior tree modification using React Native and web technologies. The architecture successfully bridges mobile applications with robot control systems while maintaining performance suitable for real-time interaction. Second, we have demonstrated through rigorous experimentation that web technologies can effectively support real-time robot control applications. The measured performance (168.88ms latency, 100% success rate) meets the requirements for responsive human-robot interaction. Third, we have established a new benchmark for robot programming efficiency, showing a 7.4x improvement over traditional approaches. This represents a significant advancement in making robot programming more accessible and efficient.
8.1 Practical Applications
The ROS-React framework has immediate applications in several domains:
i. Research and Education: Enables students and researchers to quickly prototype and test robot behaviors without deep expertise in traditional robotics programming.
ii. Industrial Robotics: Supports rapid adaptation of robot behaviors in manufacturing environments where production requirements frequently change.
iii. Service Robotics: Facilitates behavior adjustments for robots operating in dynamic human environments where unpredictable situations may arise.
8.2 Future Research Directions
Based on our findings and limitations, we identify several promising directions for future work:
a) Enhanced Collaboration: Developing multi-user editing capabilities with conflict resolution mechanisms for team-based robot programming.
b) Advanced Visualization: Integrating augmented reality interfaces for more intuitive behavior tree visualization and modification.
c) Machine Learning Integration: Incorporating AI-assisted behavior generation and optimization suggestions based on operational history.
d) Edge Computing: Extending the framework to support edge computing scenarios with limited connectivity, important for field robotics applications.
e) Formal Verification: Developing automated verification methods to ensure behavior tree modifications maintain safety and performance guarantees.
f) Real-World Deployment: Transitioning from simulation to physical robot testing in varied environmental conditions.
8.3 Concluding Remarks
The ROS-React framework represents a significant step toward more accessible and efficient robot programming. By leveraging modern web technologies and embracing dynamic modification capabilities, we have demonstrated that robot behavior development can be both powerful and user-friendly. The framework's performance characteristics and architectural design provide a solid foundation for future innovations in human-robot interaction and robot programming methodologies.
As robots become increasingly prevalent in everyday environments, tools that lower the barrier to robot programming while maintaining performance and safety will become increasingly valuable. This work contributes to that important goal and opens new possibilities for how humans and robots interact and collaborate.
The complete implementation, including all source code and documentation, has been made available to the research community to encourage further development and adoption of this approach. The full code base is in my GitHub profile at < https://github.com/KingsleyTechie/ros-react-behavior-trees >
9. Ethical Declaration
This research was conducted in accordance with academic integrity standards and transparency principles. The work presents no ethical concerns as it involves simulated robotics environments without human participants, physical robotic hardware, or sensitive data collection.
All development and testing occurred in controlled simulation environments, eliminating risks associated with physical robot operations. The research did not involve any human subjects, personal data collection, or interactions that would require ethics committee approval.
The authors declare no conflicts of interest related to this work. All code developed for this research will be made publicly available to ensure reproducibility and support open scientific discourse.
No external funding was received for this research, and the authors affirm the independence and objectivity of the findings presented.
A
A
References
Cirillo M (2020) & others. A framework for safe behavior trees. IEEE Robotics and Automation Magazine, 27(3), 76–85. https://doi.org/10.1109/MRS.2020.3003524
Colledanchise M, Ögren P (2017) How behavior trees modularize hybrid control systems and generalize sequential behavior compositions, the subsumption architecture, and decision trees. IEEE Trans Robot 33(2):372–389. https://doi.org/10.1109/TRO.2016.2633567
Colledanchise M, Ögren P (2018) Behavior trees in robotics and AI: An introduction. IEEE Trans Robot 34(4):901–918. https://doi.org/10.1109/TRO.2018.2836909
Crick C (2012) & others. Robot web tools: Efficient messaging for cloud robotics. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 4530–4537). https://doi.org/10.1109/IROS.2012.6385716
Crick C (2017) & others. ROSbridge: ROS for non-ROS users. In Robotics Research (pp. 45–64). Springer International Publishing. https://doi.org/10.1007/978-3-319-54927-9_3
Dieber B (2017) & others. Safe and secure remote robotics. IEEE Robotics and Automation Letters, 2(4), 1861–1868. https://doi.org/10.1109/LRA.2017.2719704
Johnson M (2021) & others. A React-based framework for dynamic user interfaces in robotics. Proceedings of the ACM International Conference on Human-Robot Interaction (pp. 245–254). https://doi.org/10.1145/3459995.3460702
Marzinotto A (2014) & others. Dynamic behavior trees for multi-agent task planning. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 3397–3402). https://doi.org/10.1109/IROS.2014.6942595
Quigley M (2009) & others. ROS: An open-source robot operating system. ICRA Workshop on Open Source Software
Thomas G (2022) & others. Dynamic software updates for ROS 2. IEEE Robotics and Automation Letters, 7(3), 7907–7914. https://doi.org/10.1109/LRA.2022.3186872
Appendix- Figures
A. Comprehensive latency analysis
B. Scalability analysis
C. Performance comparison
D. System architecture
