Jichen Guo

About Me

I am a Robotics and AI researcher specializing in Machine Learning, Computer Vision, and Robotics. I obtained my Bachelor’s and Master’s degrees in Communication and Information Technology from Shandong University and the University of Bremen. For 4.5 years, I worked as a scientific researcher at the University of Bremen’s Robotics Research Group, collaborating closely with the DFKI Robotics Innovation Center. My work has spanned multiple projects in robotics and AI, and I am now seeking new opportunities to apply my expertise and contribute to advancing the Robotics and AI field.

Projects

• Master Project – open/closed mouth state detection for robot-assisted drinking task, part of the BMBF project MobILe
• Master Thesis – modification of YOLOv3 for object distance estimation, part of the EU project SMART
• Student Job at BIBA – ergonomic analysis at manual assembly station
• INSYS (Interpretable Monitoring Systems) – to build interpretable self-monitoring robotic systems for safe rover exploration
• PhysWM (Learning from Causal Physical World Models) – to reduce sample and model complexity of robot learning algorithms
• MODKOM (Modular components as Building Blocks for application-specific configurable space robots) – to develop modular construction kits that allows robotic systems to be flexibly adapted to different mission scenarios

Videos

Publications

• Alvarez, Mariela De Lucas *, Jichen Guo *, Raul Domínguez and Matias Valdenegro-Toro. Terrain Classification Enhanced with Uncertainty for Space Exploration Robots from Proprioceptive Data. In Journal of LatinX in AI (LXAI) Research at ICML, 2023.
• Otto, Marc *, Octavio Arriaga *, Chandandeep Singh *, Jichen Guo *, and Frank Kirchner. PhysWM: Physical World Models for Robot Learning. In NeSy 2023: 17th International Workshop on Neural-Symbolic Learning and Reasoning, (NeSy-2023), 3.7.-5.7.2023, Certosa di Pontignano, Siena, CEUR Workshop Proceedings, Jul/2023.
• Arriaga, Octavio, Jichen Guo, Rebecca Adam, Sebastian Houben, and Frank Kirchner. Bayesian Inverse Graphics for Few-Shot Concept Learning. In International Conference on Neural-Symbolic Learning and Reasoning 2024, (NeSy-2024), Springer, pages 141-165, 2024. Springer.
• Arriaga, Octavio, Proneet Sharma, Jichen Guo, Marc Otto, Siddhant Kadwe, Rebecca Adam. Differentiable Inverse Graphics for Zero-shot Scene Reconstruction and Robot Grasping. Under revision in the Journal Robotics and automation Letters (RAL), 2025.

Contact

📧 jichen.guo [at] outlook.com 💻 GitHub | 🔗 LinkedIn