Robot learning is driven by adaptive algorithms and comprehensive training in both virtual and real-world settings. This lets humanoid robots acquire and refine intricate skills like bipedal locomotion, object manipulation, and social interactions.
Developers use an optimized software stack that includes data ingestion and processing pipelines, training frameworks, and containerized microservices to power scalable and efficient training. AI foundation models, simulation environments, synthetic data, and specialized learning techniques such as reinforcement learning and imitation learning are used to train robots to perform tasks like grasping objects or navigating obstacles in different scenes.
Training uses digital twins that accurately simulate real scenarios, providing a risk-free environment for robot models to learn and improve. This eliminates the risk of physical damage and enables faster iteration by training many different models simultaneously. In simulations, operators can easily introduce variability and noise into scenes, giving robot models a richer set of experience data to learn from.
Once the robot’s skills are adequately refined in the digital world, the models can be deployed on the real robot. In some cases, training continues with the robot operating and practicing in the real world.
Important emerging humanoid robot training techniques include:
- Machine Learning: Humanoid robots are equipped with machine learning algorithms that let them analyze data to learn from past actions and process data from sensors to make informed decisions in real time.
- Imitation Learning: Robots can acquire new skills by replicating movements demonstrated by humans. These actions are captured by sensors or cameras, then translated into robotic commands that mimic the observed behaviors. This approach is especially useful for teaching robots nuanced, complex tasks that are difficult to codify with traditional programming methods.
- Reinforcement Learning: In this technique, an algorithm uses a mathematical equation to reward robots for correct actions and penalize them for incorrect actions. Through trial and error and the associated reward system, the robot adapts and improves its performance over time.