🚀 Beyond The GSOC: Continuing Open-Source Contributions with JdeRobot

As my Google Summer of Code (GSoC) journey comes to a close, I am thrilled to continue contributing to the JdeRobot open-source ecosystem. This experience has not only strengthened my understanding of robotics application development but has also deepened my appreciation for collaborative, community-driven software development. I have been involved in jdeRobot since June 2024. My key contributions include migrating the Ace Editor to Monaco Editor and developing the Webrtc webcam driver for the computer vision exercise.

Moving forward, my next focus will be on enhancing the Robotics Academy exercises and exploring advanced deep learning applications in robotics. For the Robotics Academy, I plan to improve the existing set of exercises by making them more interactive and aligned with real-world robotics challenges.

Additionally, I intend to contribute to the documentation, testing frameworks, and community support initiatives within JdeRobot, ensuring that the platform remains accessible, robust, and welcoming to new contributors.

Through these efforts, I aspire not only to strengthen my technical expertise but also to advance JdeRobot’s mission of providing open, practical, and educational tools for robotics research and learning worldwide.

Plan & Roadmap:

1. Follow-Line Exercise with Ackermann Steering

Building on the work I did during GSoC, my immediate goal is to extend the Follow-Line exercise to Ackermann steering f1 car, which mimic the steering mechanism used in real cars.

Key Tasks:
  • Collect new datasets for the Ackermann steering F1 car in the four circuits, capturing images along with linear and angular velocity commands.
  • Train deep learning models specifically for Ackermann steering F1 car, using architectures like NVIDIA PilotNet or ResNet for imitation learning.
  • Test the models in simulation, evaluate performance, and optimize for better lane-following behavior.
Expected Outcome:

A robust, realistic Follow-Line exercise that supports Ackermann vehicles, bridging the gap between simulation and real-world car-like robotics.

2. Deep Learning-Based Robotics Exercises

In the longer term, I plan to contribute to multiple advanced exercises in the Robotics Academy, including:

  • Drone Navigation: Training models for autonomous flight in simulated environments.
  • Follow-Person Exercise: Vision-based tracking of a human target.
  • Obstacle Avoidance: Teaching robots to navigate safely in dynamic environments.
  • Follow Road/Lane: Autonomous driving along complex road networks.
  • Auto Parking: Deep learning-based parking maneuvers for cars.
  • Monte Carlo Visual/Laser Localization:Applying probabilistic localization techniques using visual and LiDAR inputs.
Expected Outcome:

A library of deep learning exercises that allow students and developers to experiment with cutting-edge AI techniques in robotics simulation.

3. Experimentation with Different Deep Learning Models

I will explore various deep learning architectures for these exercises to maximize performance:

  • CNN-based models for perception tasks (e.g., PilotNet, ResNet, EfficientNet).
  • Hybrid architectures combining CNNs and attention mechanisms for improved decision-making.
  • Optimizing model inference for GPU and CPU execution within Docker environments.
  • Sharing reproducible pipelines for dataset preprocessing, model training, and deployment.
Expected Outcome:

Higher-performing models capable of generalizing across circuits and scenarios, along with insights on model selection, training strategies, and optimization techniques.

5. Community Engagement and Documentation

Finally, I will continue contributing to documentation, tutorials, and community resources, ensuring that every exercise is easy to understand, well-documented, and reproducible.

Expected Outcome:

A stronger open-source community, more contributors, and better accessibility for learners worldwide.

Conclusion

My post-GSoC plan is centered on advancing deep learning applications in robotics, extending existing exercises, experimenting with new model architectures, and sharing all progress with the JdeRobot community. This roadmap not only enhances my skills but also ensures long-term value for learners, researchers, and developers in the open-source robotics and software ecosystem.

References

[1] End-to-End Visual Control Exercise

[2] ResNet

[3] PilotNet