Robotics
Active perception
Definition
Active perception is perception in which a robot chooses motions or interactions partly to obtain more useful observations. Examples include moving a camera to reveal an occluded object or touching an object to reduce uncertainty about its state.
Also known as: Active robotic perception
Updated
The sensing action is part of the decision
Passive perception processes whatever observations arrive from a fixed sensing arrangement. Active perception also decides how to acquire evidence. The robot may turn its head, move a wrist camera, change its base position, alter lighting, or make controlled contact so that an uncertain quantity becomes easier to estimate.
Bajcsy's 1988 paper established active perception as a problem in which sensing strategies are adjusted according to the current interpretation of the scene and the task. The action is selected for its expected information value, not only for immediate physical progress.
Occlusion makes viewpoint choice practical
A general-purpose robot often works among shelves, containers, hands, and other objects that block a single view. An active system can use its current belief to choose a next view rather than scanning every direction. In a 2026 preprint, Lee and colleagues report a grasping system that moves one wrist-mounted RGB-D camera to seek targets hidden by occlusion. The reported success rates apply to their tasks, hardware, baselines, and evaluation scenes.
Active perception can also support simultaneous localization and mapping by choosing views that reduce pose or map uncertainty. Merely moving while a camera records is not enough to make a method active; the sensing consequence must influence the action choice.
Information has a physical cost
An informative view may require extra time, energy, or motion through a constrained space. The expected observation can still be blocked, blurred, or misinterpreted. A policy that seeks information must therefore trade sensing value against collision risk and task delay.
Active perception does not guarantee that the resulting estimate is correct. Its value depends on the uncertainty model, candidate actions, sensor calibration, and how well predicted observations match the real scene.
Sources
Related terms
Sensor fusion
Sensor fusion combines information from multiple sensors or estimation sources to produce a shared estimate. The combination must account for coordinate frames, timing, uncertainty, and dependence between inputs.
Pose estimation
Pose estimation determines the position and orientation of an object or robot relative to a reference frame. For a rigid body in three-dimensional space, a full pose has three translational and three rotational degrees of freedom.
Robotic manipulation
Robotic manipulation is the use of a robot to change an object's position, orientation, or state through physical interaction. It includes grasping and moving objects as well as actions such as pushing or carrying them without a grasp.
Simultaneous localization and mapping
Simultaneous localization and mapping is the joint estimation of a robot's state and a map of its environment from sensor observations. It is commonly abbreviated SLAM.