Hardware & control
Feedback control
Feedback control is a closed-loop control method that measures a system’s current output or state, compares it with a target and adjusts the command using the resulting error. In robotics, feedback can correct joint, end-effector, balance or force errors as new sensor measurements arrive.
Also known as: closed-loop control, feedback control system, feedback control systems
Updated
Closing the loop
A feedforward command is calculated from a model or planned trajectory before its result is measured. Feedback adds a repeated correction:
error = target - measured_output
command = feedforward_command + controller(error)Åström and Murray describe feedback as a way to reduce sensitivity to disturbances and uncertainty, provided the loop is designed with suitable stability and performance margins.
Feedback on a robot
A joint controller can compare requested and measured joint position or velocity. A Cartesian controller can compare target and estimated end-effector pose. Whole-body controllers can feed back base orientation, centre-of-mass estimates and contact state.
Proportional, proportional–integral–derivative and model-based controllers use different rules to turn error into a command. Modern Robotics Chapter 11 derives robot motion controllers using measured state and a model of the mechanism.
What feedback does not guarantee
Feedback cannot correct information that sensors do not expose, and delayed or noisy measurements can destabilise a fast loop. Gains and control frequency must match the robot, actuator limits and task dynamics. Contact changes, saturating motors and an inaccurate state estimate can invalidate assumptions used by the controller.
A learned policy may operate inside a feedback loop or provide targets to a lower-level controller. Calling a policy “closed loop” therefore does not say which errors are measured, which component corrects them or what safety limits are enforced.
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Related terms
Models & learning
Policy
A policy is the decision rule that maps a robot’s current observations or estimated state, and sometimes a task instruction, to an action or probability distribution over actions. It can be hand-designed or learned from demonstrations, rewards or both. In humanoid robotics, its outputs may be joint targets, torques, end-effector changes or higher-level skills.
Hardware & control
Proprioception
Proprioception is sensing of a robot’s own internal configuration and motion rather than the external scene. For a humanoid it commonly includes joint positions and velocities, actuator effort or torque, and inertial measurements of body rotation and acceleration. These signals support state estimation and feedback control but do not, by themselves, directly describe nearby objects or terrain.
Hardware & control
Impedance control
Impedance control regulates the dynamic relationship between a robot’s motion error and the force or torque it exerts, usually by giving the robot virtual stiffness, damping and sometimes inertia. It lets an end effector or limb yield predictably during contact instead of trying to hold an exact position regardless of interaction forces.
Hardware & control
Torque control
Torque control is a control mode in which desired joint torques, or equivalent linear forces, are the command variables sent to a robot’s low-level actuators. Unlike position control, it does not directly command where each joint should be. Motion and contact instead emerge from the applied efforts, robot dynamics, feedback, constraints and any higher-level controller.
Hardware & control
Whole-body control
Whole-body control coordinates a humanoid’s full multibody state, actuated joints and environmental contacts to pursue several motion or force objectives while respecting constraints such as balance, joint limits and friction. It is a family of hierarchical or optimisation-based methods, not one algorithm; implementations may output joint positions, accelerations or torques.