How is AI reasoning applied in robotics?

AI reasoning in robotics enables machines to make decisions, solve problems, and adapt to dynamic environments by processing data and applying logical or probabilistic models. At its core, AI reasoning involves algorithms that interpret sensor inputs, infer context, and generate actions. For example, a robot navigating a cluttered room uses reasoning to map its surroundings, identify obstacles, and plan a collision-free path. This process often combines techniques like symbolic logic (defining rules for decision-making) and statistical methods (handling uncertainty) to balance precision with real-world unpredictability.

A key application is autonomous navigation. Robots like warehouse AGVs (Automated Guided Vehicles) use AI reasoning to interpret lidar and camera data, distinguish between static and moving objects, and adjust routes in real time. Algorithms such as A* or RRT (Rapidly Exploring Random Trees) handle path planning, while probabilistic approaches like Bayesian filters (e.g., Kalman or particle filters) track the robot’s position and predict environmental changes. For instance, an AGV might reroute around a suddenly appearing forklift by recalculating its path using updated sensor data. These systems often integrate modular architectures—like the Robot Operating System (ROS)—to separate perception, planning, and control layers while ensuring coordinated reasoning across components.

Another example is task planning in industrial robots. Consider a robotic arm assembling a complex device: AI reasoning determines the sequence of actions (e.g., picking components, tightening screws) based on predefined assembly rules and real-time feedback. Hierarchical task networks (HTNs) or PDDL (Planning Domain Definition Language) models break high-level goals into executable steps. If a screw is misaligned, the robot might use force-torque sensors to detect the error, reason about possible causes (e.g., incorrect angle), and adjust its motion. Modern implementations often combine classical planning with machine learning, where neural networks refine motion policies through trial and error, improving efficiency over time. This hybrid approach allows robots to handle both structured workflows and novel scenarios.