How are AI agents used in robotics?

AI agents are software components that enable robots to perceive their environment, make decisions, and execute actions autonomously or semi-autonomously. In robotics, these agents typically combine sensors, algorithms, and actuators to solve problems in dynamic or unstructured environments. For example, an AI agent in a warehouse robot might process camera and lidar data to navigate around obstacles, prioritize tasks like item retrieval, and adjust paths in real time. The core idea is to create systems that can adapt to changing conditions without explicit reprogramming, relying instead on machine learning (ML) models, rule-based logic, or hybrid approaches.

One common application is autonomous navigation. AI agents use techniques like simultaneous localization and mapping (SLAM) to build environment maps while tracking the robot’s position. For instance, delivery robots in hospitals employ SLAM with depth sensors to avoid collisions and optimize routes. Another example is industrial robots in manufacturing: AI agents analyze vision data to identify defective products on a conveyor belt, then direct robotic arms to remove them. Reinforcement learning (RL) is also used to train agents for tasks like grasping objects with varying shapes, where trial-and-error simulations teach the robot how to adjust grip strength and orientation.

AI agents also enable human-robot collaboration. Collaborative robots (cobots) in assembly lines use agents to interpret human gestures or voice commands, adjusting their behavior based on context. For example, a cobot might slow its movements when a worker is nearby for safety. In research, humanoid robots like Boston Dynamics’ Atlas use AI agents to balance, walk, or perform parkour by combining sensor fusion (e.g., inertial measurement units) with predictive control algorithms. These systems often rely on neural networks trained on large datasets of motion trajectories to generalize across scenarios. While powerful, such implementations require careful integration with hardware to ensure low-latency responses, which is why frameworks like ROS (Robot Operating System) are widely used to manage communication between agents and actuators.