# Robot Learning Fundamentals[#](https://docs.nvidia.com/learning/physical-ai/getting-started-with-isaac-lab/latest/robot-learning-fundamentals/index.html.md#robot-learning-fundamentals 'Link to this heading')

In this module, you’ll explore the concepts you’ll use throughout the learning path: how to specify a robot learning task, learn and evaluate a policy, and prepare for transfer from simulation to a real robot. You’ll then apply these ideas in [Isaac Lab Basics](https://docs.nvidia.com/learning/physical-ai/getting-started-with-isaac-lab/latest/isaac-lab-basics/index.html.md). Throughout these lessons, you’ll see different diagrams describing the same concepts but at different levels of abstraction—try to connect the ideas across the diagrams and lessons.

If you’re ready to start running code, start with [Isaac Lab Basics](https://docs.nvidia.com/learning/physical-ai/getting-started-with-isaac-lab/latest/isaac-lab-basics/index.html.md) or [Train a Robot Arm](https://docs.nvidia.com/learning/physical-ai/getting-started-with-isaac-lab/latest/train-a-robot-arm/index.html.md) and refer back to this module as needed.

[An agent contains a policy and learning algorithm. Observations feed the policy, rewards feed the learning algorithm, and the learning algorithm updates the policy. Policy actions return to an environment containing an articulated robot arm.](https://docs.nvidia.com/learning/physical-ai/getting-started-with-isaac-lab/latest/_images/robot-learning-loop.svg)

The policy acts in the environment while the learning algorithm uses observations and rewards to update it.[#](https://docs.nvidia.com/learning/physical-ai/getting-started-with-isaac-lab/latest/robot-learning-fundamentals/index.html.md#id1 'Link to this image')

By the end of this module, you’ll understand the concepts shown in the figure.

Learning Objectives

- Define a robot learning task’s observations, actions, rewards, resets, episode endings, and success criteria.

- Compare supervised, imitation, unsupervised, and reinforcement learning by how they learn from data.

- Describe how Proximal Policy Optimization (PPO) collects experience and updates an actor and critic, and how parallel simulation supports training.

- Plan a repeatable policy evaluation that checks task success as well as reward.

- Explain how a state-based teacher trains a camera-based student and why imitation alone does not prove task success.

- Identify simulation-to-reality mismatches and ways to address them, including checks on the real robot.

Use the _activities_ to check your understanding as you go. Here are some tips for completing them:

- Make a prediction about the outcome of a command before running it.

- After running a command, change one thing and describe what you observe.

- Answer the understanding question before opening its answer or hint.

- Use an agent to quiz yourself on the learning objectives.