Robots use artificial intelligence (AI) to adapt to new environments by combining sensor data, machine learning models, and real-time decision-making. At a high level, robots rely on sensors like cameras, LiDAR, or infrared to gather information about their surroundings. AI algorithms process this data to build a dynamic understanding of the environment, allowing the robot to adjust its behavior. For example, a robot vacuum might map a room using SLAM (Simultaneous Localization and Mapping) and then update its path when it detects new obstacles like furniture or toys. This process involves continuous feedback loops where the robot learns from interactions and refines its actions over time.
A key technique is the use of reinforcement learning (RL), where robots learn optimal behaviors through trial and error. For instance, a warehouse robot tasked with moving packages might initially collide with unexpected objects but gradually learn to navigate around them by receiving rewards for efficient paths and penalties for collisions. Another example is computer vision models trained to recognize unfamiliar objects. A delivery robot encountering a new type of barrier (e.g., construction scaffolding) could use pre-trained object detection models to classify the obstacle and adjust its route, even if it wasn’t explicitly trained on that specific scenario. These models are often fine-tuned in real time using edge computing, enabling quick adaptations without relying on cloud-based processing.
To maintain adaptability, robots often employ modular AI architectures. For example, a robot designed for industrial inspection might separate its navigation system from its defect-detection system. If the lighting conditions change, the vision module can adjust exposure settings independently while the navigation module continues using LiDAR data. Additionally, techniques like transfer learning allow robots to apply knowledge from one environment to another. A drone trained to fly in open fields could adapt its flight controller to urban environments by retraining only specific layers of its neural network, preserving general skills like obstacle avoidance. By combining these approaches, robots balance specialized task performance with the flexibility to handle unexpected changes.