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Achievement: YOLOAchievement: StarstruckAchievement: Pair ExtraordinaireAchievement: QuickdrawAchievement: Pull Sharkx2Achievement: Arctic Code Vault Contributor

Achievements

Achievement: YOLOAchievement: StarstruckAchievement: Pair ExtraordinaireAchievement: QuickdrawAchievement: Pull Sharkx2Achievement: Arctic Code Vault Contributor

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abmoRobotics/README.md

Hello 👋

  • 👨‍💼 I am a PhD student from Aalborg University in Denmark, with a background in Robotics, AI & learning-based navigation using reinforcement learning.
  • 📖 I'm a passionate about artificial intelligence, reinforcement learning, and scaling these systems to generalize to real-world robotic applications.

📫 Contact with me:

[daniel-mparis](https://www.linkedin.com/in/anton-bjorndahl/) antonbm2008@gmail.com

📚 Main Programming Languages:

python cplusplus csharp

Current Work:

Building on Isaac Lab and RLRoverLab, i am currently working on improving the scalability and generalizability of AI agents for navigation. The goal is to significantly enhance real-world performance and bridge the gap between simulation and reality (Sim2Real). To achieve these objectives, I employed a range of state-of-the-art methods, including:

  • Learning By Cheating.
  • Offline Reinforcement Learning
  • Combining Real World Datasets with Synthetic Datasets
  • Hybrid Policies

Learning by Cheating is used to train the agent using a simple and computationly fast observation space (heightmap), and transfer the learned policy to a model that uses RGB-D information instead of a heightmap.

inference.mp4

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