Robots are controlled through layers of hardware and software that work together: sensors detect what’s happening, a controller decides what to do, and actuators (motors, grippers, wheels) carry out the action. Some robots follow pre-programmed instructions with no ability to adapt, while others continuously adjust their behavior based on real-time feedback. The method depends on the robot’s purpose, from a fixed factory arm repeating the same weld to a surgical system responding to a surgeon’s hand movements.
Open-Loop vs. Closed-Loop Control
The most fundamental distinction in robot control is whether the system checks its own work. In an open-loop system, the robot receives a command and executes it without verifying the result. Think of a simple conveyor belt motor set to run at a fixed speed. It doesn’t measure whether it’s actually hitting that speed; it just applies the same power every cycle. Open-loop control is cheaper, simpler, and faster because there’s no sensing or processing delay. It works well in predictable environments where conditions don’t change much.
Closed-loop control adds a feedback step. The robot monitors its output through sensors, compares it to the desired result, calculates the error, and adjusts. A mobile robot navigating a warehouse, for example, continuously reads data from cameras, wheel encoders, and gyroscopes to check whether it’s on course, then corrects its motor commands accordingly. This feedback loop makes the robot far more accurate and adaptable, but it introduces a small delay between sensing, processing, and acting. For most real-world applications, that tradeoff is worth it.
The Hardware That Runs the Show
At the lowest level, a robot’s movements are managed by a dedicated computing device. In hobbyist and lightweight applications, that’s often a microcontroller, a small chip embedded directly inside the robot that accepts input from sensors and sends signals to motors or other components. Arduino boards are a common example. Microcontrollers are flexible and inexpensive, but they require custom programming for safety features and fault monitoring.
Industrial robots more commonly use Programmable Logic Controllers (PLCs). A PLC accepts information from connected sensors and input devices, processes that data, and triggers outputs like relays, valves, or motor drives based on preset rules. What makes PLCs especially suited for factories and high-stakes environments is their built-in safety monitoring. Hardware and software “watchdogs” continuously check the system during every processing cycle. If a scan doesn’t finish in the allotted time, the watchdog faults the PLC and places it into a safe mode while notifying the operator. You can add similar protections to a microcontroller, but you’d have to write those programs from scratch.
Sensors That Provide Feedback
A robot’s ability to control itself depends entirely on the quality of information it receives. Three sensor types appear across nearly every mobile or industrial robot.
- Wheel encoders measure how far and how fast a wheel has turned. A typical encoder produces 1,000 electrical pulses per revolution, and the controller converts those pulses into precise distance and speed calculations. They’re the most basic form of position tracking.
- Inertial Measurement Units (IMUs) contain a 3-axis accelerometer and a 3-axis gyroscope. The gyroscope measures how fast the robot is rotating in three dimensions, while the accelerometer tracks changes in speed and direction. Combining these readings lets the robot estimate its orientation and heading.
- LIDAR fires laser pulses at the surroundings and measures how long the reflected light takes to return. This produces a 2D or 3D map of points representing nearby objects, walls, and obstacles.
No single sensor is perfect. Wheel encoders fail when tires slip. IMUs gradually drift over time. LIDAR can be fooled by reflective surfaces. To compensate, robots fuse data from multiple sensors using statistical filters. A common approach called the Extended Kalman Filter combines IMU readings with wheel encoder data, using each source to correct the other’s weaknesses. The LIDAR map then serves as an additional check against the fused estimate, giving the robot a reliable picture of where it is.
How PID Algorithms Correct Errors
Once sensors detect an error (the robot is drifting left, a joint is moving too slowly), something has to decide how aggressively to correct it. The workhorse algorithm for this is PID control, which stands for Proportional, Integral, and Derivative. Nearly every robot with closed-loop control uses some version of it.
The proportional component pushes the robot toward its target in proportion to how far off it is. If you’re 10 degrees off course, you get a bigger correction than if you’re 2 degrees off. The derivative component acts like a damper: it looks at how quickly the error is changing and slows the correction down to prevent overshooting. Without it, the robot would oscillate back and forth around its target. The integral component tracks the total accumulated error over time. If a small, persistent drift keeps the robot slightly off target, the integral term builds up and eventually pushes hard enough to eliminate it.
Tuning these three values against each other is one of the core challenges in robotics. Too much proportional gain and the robot overcorrects wildly. Too much derivative and it responds sluggishly. The right balance depends on the robot’s weight, speed, and the task at hand.
Software Architecture: How Components Talk
A modern robot might have dozens of separate software processes running simultaneously: one reading the camera, another planning a path, another controlling a gripper. These all need to share data without stepping on each other. The Robot Operating System (ROS), an open-source framework widely used in research and industry, solves this with a structured communication layer.
In ROS, every software process is a “node.” A camera driver is one node, a path planner is another, a motor controller is a third. Nodes communicate by publishing messages to named channels called “topics.” A LIDAR node might publish scan data to a topic called “laser_scan,” and any other node that needs that data simply subscribes to the same topic. Publishers and subscribers don’t need to know about each other, which makes it easy to swap components in and out. For tasks that need a direct request-and-response exchange (asking a mapping node for the robot’s current position, for example), ROS provides “services” that work more like a phone call than a broadcast.
Direct Human Control
Many robots are controlled directly by a human operator rather than acting autonomously. The simplest version is a joystick or gamepad sending movement commands to a remote robot. More sophisticated systems use a master-slave arrangement, where the operator manipulates a controller device and the robot mirrors those movements at a distance.
Surgical robots are the most refined example. Systems like the da Vinci allow a surgeon to control robotic instruments inside a patient’s body from a console across the room. The robot translates the surgeon’s hand movements into smaller, more precise motions at the instrument tip. A critical feature in these systems is haptic feedback, where forces encountered by the robot’s instruments are relayed back to the surgeon’s hands so they can feel tissue resistance. The most common approach uses impedance control, where virtual forces connect the master controller and the remote robot, causing them to track one another. When the robotic instrument presses against tissue, the surgeon feels a corresponding push on the controller. Some systems also display a visual representation of forces in real time when direct haptic feedback isn’t available.
AI and Learned Control
Traditional control methods require engineers to define rules for every situation the robot might encounter. Machine learning flips this: the robot learns control strategies through experience. Deep reinforcement learning, in particular, has been successfully applied to robotic arm trajectory planning and motion control. The robot tries actions in a simulated or real environment, receives rewards for getting closer to the goal and penalties for mistakes, and gradually develops a policy for how to move.
Recent work has used deep reinforcement learning to plan smooth trajectories for robotic arms, solving problems that are mathematically complex with traditional approaches, like figuring out how a six-jointed arm should move each joint to reach a specific point in space. These systems use paired neural networks (one to choose actions, another to evaluate how good those actions were) to improve stability and accuracy during training. The resulting movements can then be smoothed with curve-fitting techniques to eliminate the jittery, hop-like motions that raw learning algorithms sometimes produce.
An even newer layer involves large language models. Researchers have built systems where an operator speaks a natural language command (“hold this steady while I cut”), and an LLM interprets the intent, selects the appropriate robotic skill, and dispatches the action. One such framework uses speech-to-text conversion, processes the text through a language model to understand what the operator wants, and then triggers the matching pre-built manipulation skill on a robotic arm. The tradeoff is latency: the pipeline of converting speech, interpreting intent, and dispatching commands adds noticeable delay, which limits use in fast-paced tasks.
Brain-Computer Interfaces
At the frontier of robot control, brain-computer interfaces (BCIs) translate neural signals directly into robotic movement. A 2025 study published in Nature Communications demonstrated a noninvasive system where participants wearing EEG caps controlled a robotic hand at the individual finger level. When a person imagined moving a specific finger, a deep neural network decoded the resulting brain signals (specifically, patterns in the 8 to 13 Hz alpha band over the motor cortex) and mapped them to corresponding robotic finger motions. Earlier work had already shown EEG-based control of robotic arms for reaching and grasping in three-dimensional space. These systems are still primarily in research, but they point toward a future where people with paralysis or amputations could control prosthetic limbs through thought alone.
Safety Controls for Robots Near People
When robots share space with humans, control systems need an additional safety layer. Collaborative robots (cobots) use force and torque sensors in their joints to detect unexpected contact. The control strategy called Power and Force Limiting keeps the forces a robot can exert within thresholds designed to prevent injury. If the robot’s arm bumps a person, the sensors detect the spike in force and the controller immediately stops or reverses the motion. Testing standards developed by the Robotic Industries Association and referenced by OSHA describe specific methods for verifying that a cobot’s forces stay within allowable limits during every possible type of contact.