Isaac Lab Training with Camera Data

Status
Open

Goal: Learn how to train reinforcement learning agents in Isaac Lab using camera observations instead of just state-based inputs.

Outline:

1. Why Camera Training

  • Brings policies closer to real-world robotics (vision-driven).
  • Enables learning from RGB or depth cameras.
  • Useful for sim-to-real transfer and more complex perception tasks.

2. How It Works

  • Choose a camera-enabled task (for example: Cartpole RGB Camera Direct).
  • During training, the policy receives images from the simulated camera.
  • The rest of the RL loop (rewards, updates, logging) stays the same as in state-based training.

3. Training Flow

  1. Select a task that supports cameras.
  2. Launch training with cameras enabled.
  3. Observe how the agent learns to act directly from visual input.
  4. Evaluate performance in simulation by replaying trained policies.

Takeaway

Any camera-enabled task (e.g. RGB Camera Direct) can be trained in Isaac Lab. This makes it easy to extend standard RL setups to vision-based control and prepare agents for more realistic robotics scenarios.