Robots use artificial neural networks (ANNs) to process sensory data, make decisions, and execute tasks by mimicking how biological brains recognize patterns. ANNs are composed of interconnected layers of nodes (neurons) that transform input data—like images, sensor readings, or commands—into actionable outputs. For example, a robot arm sorting objects on a conveyor belt might use a convolutional neural network (CNN) to identify items via a camera. The CNN processes pixel data through layers, detecting edges, shapes, and textures to classify objects. Once identified, the robot’s control system uses this classification to guide its gripper to pick and place items accurately. This process replaces rigid, preprogrammed rules with adaptive learning, enabling the robot to handle variations in object appearance or positioning.
In dynamic environments, ANNs help robots adapt to changing conditions. Reinforcement learning (RL) is often used here, where the robot learns through trial and error by receiving rewards for successful actions. For instance, an autonomous drone navigating a cluttered warehouse might use an ANN trained with RL. The network takes inputs from LiDAR and cameras to map obstacles and adjust flight paths in real time. During training, the drone receives positive rewards for avoiding collisions and reaching targets efficiently, allowing the ANN to refine its decision-making over time. This approach is especially useful for tasks where explicit programming is impractical, such as handling unpredictable human interactions or environmental shifts. Recurrent neural networks (RNNs) can also manage sequential tasks, like a robot arm assembling parts in a specific order, by retaining memory of previous steps.
ANNs enable robots to generalize from training data to new scenarios. Transfer learning, where a pre-trained model is fine-tuned for a specific task, reduces the need for massive datasets. For example, a service robot trained to recognize common household objects (cups, books) in a simulation can adapt its vision system to real-world kitchens with minimal additional data. Additionally, collaborative robots (cobots) use ANNs to learn safe and efficient movements by observing human operators. Sensors capture motion data, which the ANN processes to predict optimal trajectories while avoiding collisions. This combination of perception, decision-making, and motion control—powered by ANNs—allows robots to perform complex tasks like package delivery, precision welding, or even assisting in surgery, where accuracy and adaptability are critical. By integrating ANNs, developers create systems that improve with experience, bridging the gap between static automation and intelligent behavior.