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Hello 👋

  • 👨‍💼 I am a robotics master student from Aalborg University in Denmark, currently working on my master thesis in learning-based mapless 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

Currently Working on:

Currently, I am working on migrating a reinforcement learning environment I've developed previously to ORBIT, and improving the scalability and generalizability of the system. The goal is to significantly enhance real-world performance and bridge the gap between simulation and reality (Sim2Real). To achieve these objectives, I am employing a range of state-of-the-art methods, including:

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

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. The video below shows the environment i am migrating to ORBIT from Isaac Gym.

Rover.1.mp4

Anton Bjørndahl Mortensen's Projects

clonelab icon clonelab

[WIP] Framework for BC, Offline RL, Learning By Cheating. The framework has Isaac Sim integration.

drl_grasping icon drl_grasping

Deep Reinforcement Learning for Robotic Grasping from Octrees

orbit icon orbit

Unified framework for robot learning built on NVIDIA Isaac Sim

p2 icon p2

P2 - Projekt manipulator control

rlroverlab icon rlroverlab

A suite of space-related reinforcement learning environments implemented in ORBIT.

skrl icon skrl

Modular reinforcement learning library (on PyTorch and JAX) with support for NVIDIA Isaac Gym, Isaac Orbit and Omniverse Isaac Gym

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