Robot learning is a collection of algorithms and methodologies that help a robot learn new skills such as manipulation, locomotion, and classification in either a simulated or real-world environment.

Robot learning is an instrumental part of the robot training process. Because training robots can be time-consuming and resource-intensive, physical training can be supplemented with training in simulated environments for physical AI. Applying robot learning techniques in simulated environments can accelerate training times and enable scalability, as many robots can learn and train simultaneously. In simulation, operators can also easily add variance and noise to each scene with a robot, giving it more experience and materials to learn from. 

There are several learning approaches that can be used to teach robots new skills in both physical and simulated environments. 

1. Reinforcement Learning: In reinforcement learning, a robot uses the power of neural networks to learn through iterative trial and error, guided by a reward function. This function provides rewards or penalties based on the robot's actions, helping it determine the best next steps. Through interaction with the environment, the robot learns which behaviors are rewarded and updates its model accordingly. Reinforcement learning is most useful in simulated environments, where robots can safely explore and learn from a wide range of scenarios without real-world consequences.
   
   The workflow for a reinforcement learning process in robotics systems follows these basic steps: 
   
   
   • Setting up a training environment: This is an environment that defines the task, either in simulation or the real world. Observations are recorded and a mathematical function is created that calculates a reward or penalty for each action taken by the agent (in this case, the robot). The recorded observations are put into a policy network, which chooses an action for the agent to take. Both the observations and rewards are stored for later use in the training cycle. 
   • Training: The action is sent back to the simulator so the rest of the environment can be updated in response. After several rounds of these forward passes, the reinforcement learning model takes a look back, evaluating whether the actions it chose were effective or not. This information is used to update the policy network, and the cycle begins again with the improved model.
   • Deploying: After training and successfully completing its actions, an AI model can be deployed to a real-world robot. The success of this transfer depends on several factors, including the gap between the virtual and real environments, the difficulty of the learning task, and the complexity of the robot platform itself.
2. Imitation Learning: In imitation learning, robots observe and replicate demonstrations from an expert—either real videos of humans or simulated data. Imitation learning uses labeled datasets and is advantageous for teaching robots complex actions that are difficult to define programmatically. It's particularly useful for humanoid robots designed to function and collaborate with humans. While recording a demonstration may be simpler than specifying a reward policy—as in reinforcement learning—creating a perfect demonstration can be challenging, and robots may struggle with unforeseen situations.
   
   The key steps of imitation learning include: 
   
   
   • Creating a demonstration: An expert demonstrates a task that's recorded. 
   • Policy training: Robots typically learn to mimic expert behavior through two approaches: 
     • Behavioral Cloning: An algorithm maps the relationship between the environment and actions taken, then mimics these relationships.
     • Inverse Reinforcement Learning: The algorithm infers the underlying reward function that the expert is optimizing for and learns the corresponding policy.
   • Policy extraction: After training, the model creates a policy that’s used for decision-making.
   • Evaluation: The trained model is then tested in the target environment and the performance is compared to the expert's demonstration. If necessary, more data is collected to adjust the model and the process is repeated.
   • Deployment: Once the learned policy performs successfully, the model can be deployed to the intended application.  


3. Diffusion Policy: Diffusion models are generative models that learn to reverse a gradual noising process to generate new data samples. Robot diffusion policy is a method that uses diffusion models to create and optimize robot actions for desired outcomes, especially in complex, high-dimensional action spaces. 

The process involves training the model on successful robot trajectories, enabling it to map from a noisy initial state to a sequence of goal-achieving actions. During operation, the model generates new action sequences by iteratively refining a noisy state, guided by a learned gradient field. This results in coherent, goal-oriented behaviors. The approach is good for multi-step robotics tasks, offering robust and adaptable robot behaviors and training stability to handle various modalities of action distributions.

Traditionally, robots were trained using pre-programming approaches. These succeeded in predefined environments but struggled with new disturbances or variations and lacked the robustness needed for dynamic real-world applications.

Use of simulation technologies, synthetic data, and high-performance GPUs has significantly enhanced real-time robot policy training. It has also provided a cost-effective way to train robots by avoiding hardware costs due to damage to the real robot and its environment upfront while efficiently running multiple algorithms in parallel. 

By adding noise and disturbances during training, smart robots learn to react well to unexpected events. This advancement is particularly beneficial for robot motion planning, movement, and control. With improved motion planning, robots can better navigate dynamic environments, adapting their paths in real time to avoid obstacles and optimize efficiency. Better robot control systems enable robots to fine-tune their movements and responses, ensuring precise and stable operations, even in the face of unexpected changes or disturbances.

These developments have made robots more adaptable and versatile, and better equipped overall to handle the complexities of the real world.

Manufacturing
Robots can learn to perform complex assembly tasks by observing human workers or through trial and error, enabling automation of complex manufacturing and assembly processes. Reinforcement learning algorithms help robots refine their movements to achieve higher precision and efficiency in tasks like welding, painting, and component assembly. They can also learn to adapt to changes in the manufacturing process, such as variations in raw materials or changes in product specifications. This adaptability is crucial for maintaining high-quality production in dynamic environments.

Retail
In a retail environment, autonomous robots equipped with computer vision models can learn to navigate store aisles, depalletize and unload inventory, and even reshelve them in their correct position in a store. Robots learn to do this with reinforcement learning with rewards for successfully completing tasks and penalties for missing the mark. Skills are further refined with imitation learning by allowing robots to imitate how human employees perform these tasks. 

Healthcare
In healthcare, robot learning can be used to teach robots specialized maneuvers such as grasping small objects like needles and passing them from place to place with precision. This can augment the skills of surgical teams for minimally invasive surgery while reducing surgeons’ cognitive load. Robot learning can also be used to train robots for patient rehabilitation tasks, such as assisting with physical therapy exercises and adapting to each patient's unique needs.

Robots need to be adaptable, readily learning new skills and adjusting to their surroundings. NVIDIA Isaac™ Lab is an open-source simulation-based modular framework for robot learning that's built on top of NVIDIA Isaac Sim™. Its modular capabilities with customizable environments, sensors, and training scenarios—along with techniques like reinforcement learning and imitation learning—lets you teach any robot embodiment to learn from quick demonstrations. 

Isaac Lab is compatible with  MuJoCo, an open-source physics engine that facilitates research and development in robotics, biomechanics, graphics and animation, and more. MuJoCo's ease of use and lightweight design allow for rapid prototyping and deployment of policies. Isaac Lab can complement it when you want to create more complex scenes, scaling massively parallel environments with GPUs and high-fidelity sensor simulations with NVIDIA RTX™ rendering. 

If you’re an existing NVIDIA Isaac Gym user, we recommend migrating to Isaac Lab to ensure you have access to the latest advancements in robot learning and a powerful development environment to accelerate your robot training efforts. Isaac Lab is open-sourced under the BSD-3 license and is available to try today on GitHub.