Machine learning (ML) is a subset of artificial intelligence that enables systems to learn patterns from data and make decisions without being explicitly programmed for every scenario. At its core, ML involves training algorithms on datasets to recognize relationships, classify information, or predict outcomes. Common approaches include supervised learning (using labeled data), unsupervised learning (finding patterns in unlabeled data), and reinforcement learning (learning through trial and error with rewards). For example, a supervised learning model might classify images of objects, while a reinforcement learning agent could learn to navigate a maze by receiving feedback on its actions.
In robotics, ML is often applied to tasks that require adaptability and real-time decision-making. A key area is perception: robots use ML to process sensor data, such as camera feeds or lidar scans, to identify objects or map environments. For instance, autonomous drones might use convolutional neural networks (CNNs) to detect obstacles during flight. Another application is control systems: reinforcement learning helps robots optimize movements, like a robotic arm learning to grasp objects of varying shapes through simulated trials. Boston Dynamics’ Spot robot, for example, uses ML to adjust its gait on uneven terrain. ML also enables robots to learn from human demonstrations, such as training a robot to assemble parts by observing a technician.
Practical challenges in applying ML to robotics include the need for large, high-quality datasets and computational constraints. Robots often operate in dynamic environments, requiring models to generalize well beyond training data. For example, a warehouse robot trained to handle boxes might struggle with irregularly shaped items unless its training data is diverse. Edge computing—running ML models directly on the robot’s hardware—is critical for real-time responses but requires efficient algorithms. Additionally, safety is a concern; a self-driving car’s ML system must make reliable decisions under uncertainty. Developers often address these challenges by combining traditional control systems (e.g., PID controllers) with ML, using techniques like imitation learning to bootstrap models with human expertise before refining them through trial and error.