Hardware & control
State estimation
State estimation is the process of inferring variables that are not known exactly—such as a robot’s base pose, velocity, contact state or sensor bias—from noisy measurements, control inputs and a model of how the system evolves. An estimator should provide both an estimate and, where possible, a representation of its uncertainty.
Also known as: robot state estimation, robotic state estimation
Updated
Sensors observe the state only indirectly
Probabilistic Robotics frames state estimation as recovering variables from partial, noisy sensor data. An encoder can measure a joint angle, but it does not directly provide a walking humanoid’s global position. An IMU measures angular velocity and specific force, not an error-free world-frame pose.
An estimator combines those observations with a process or motion model. Bayesian filters represent a belief over possible states; Kalman-family filters approximate that belief with a mean and covariance under particular assumptions. Optimisation and factor-graph methods can estimate a window or history of states instead of updating only the current one.
Humanoid estimation depends on changing contact
Leg kinematics can constrain base motion while a foot is stationary, but the constraint becomes wrong when the foot slips or lifts. Rotella and colleagues incorporate flat-foot contact constraints into a humanoid estimator without assuming a fixed gait.
Pronto combines high-rate IMU and leg odometry with lower-rate visual or lidar corrections on legged robots. This illustrates a common split: proprioceptive estimates support the fast control loop, while exteroceptive sensors reduce longer-term drift.
Estimated state is not ground truth
Every estimate depends on sensor calibration, timestamps, model assumptions, contact detection and noise settings. A smooth output can still be biased or overconfident. Covariance reports modelled uncertainty, not a guarantee that unmodelled errors are absent.
Datasets should keep raw measurements separate from filtered state and record the estimator name, version, parameters, reference frame, update rate and covariance. Simulator ground truth, motion-capture measurements and online estimates should use distinct field names so evaluation does not accidentally train on unavailable or circular targets.
Sources
Related terms
Data & collection
Robot state
Robot state is the set of variables used to describe a robot at a particular time, such as joint positions and velocities, base pose, end-effector pose, gripper state, actuator measurements, or estimated motion. In control theory, a complete state contains enough information to predict future evolution given an action; in robot datasets, “state” often means only the measured or estimated subset that was logged.
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.
Models & learning
Pose estimation
Pose estimation is the process of inferring the position and orientation of a body, object, camera, hand, or robot relative to a specified coordinate frame. In three-dimensional robotics this is often called 6D or 6-DoF pose estimation because the result has three translational and three rotational degrees of freedom, even when orientation is stored with more than three numbers.
Data & collection
Sensor calibration
Sensor calibration is the estimation and documentation of parameters that map raw sensor readings into physically meaningful values and known spatial relationships. In robotics it can include scale, bias, distortion, intrinsic camera parameters, sensor-to-sensor or sensor-to-robot transforms, and timing offsets. Calibration does not remove all noise or drift.
Hardware & control
Floating base
A floating-base model represents a robot’s root body with six unconstrained spatial degrees of freedom—three for translation and three for rotation—instead of fixing it to the world. Those base coordinates are not directly actuated; a legged robot changes them through joint motion and external contact forces.