About the Role

As a Reinforcement Learning Intern, you will help bring perception into Mirokaï's navigation stack. You will integrate depth camera and time-of-flight sensor models into our RL environments, and use these rich perceptual inputs to train navigation policies that can handle real-world obstacles and spaces. This internship offers deep hands-on experience at the intersection of sensor simulation, reinforcement learning, and sim-to-real transfer.

What You'll Be Doing

• Integrate depth camera and time-of-flight sensor models into our Isaac Lab simulation environments.
• Design observation spaces and policy architectures that leverage perceptual inputs for obstacle-aware navigation.
• Train and evaluate reinforcement learning policies conditioned on simulated sensor data.
• Analyze navigation performance, robustness to sensor noise, and sim-to-real transfer aspects.
• Integrate trained policies in...