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.
Also known as: Robot proprioception, Proprioceptive sensing
Updated
What counts as proprioceptive sensing
The Springer textbook Elements of Robotics separates proprioceptive sensors, which measure the robot itself, from exteroceptive sensors, which measure the surrounding environment. Encoders, motor-state feedback and inertial measurement units are common proprioceptive sources; cameras and range sensors are exteroceptive.
Force and contact signals sit near the boundary because they reveal an external interaction through onboard hardware. Robotics papers do not always classify them consistently, so a dataset should list the actual channels rather than rely on the word “proprioception” alone.
Why humanoids need state estimation
Joint encoders describe the articulated shape but cannot alone determine a floating humanoid’s motion through the world. Inertial measurements and kinematic contact assumptions help estimate base orientation, velocity and pose. An IROS humanoid state-estimation study used joint encoders and multiple body-mounted IMUs because the common assumption of a flat, stationary support foot breaks during heel-to-toe motion.
Learned locomotion also depends on these signals. The ANYmal sim-to-real study used proprioceptive observations and an actuator model to transfer dynamic policies from simulation to hardware.
What training data must declare
Proprioceptive streams need timestamps, units, sign conventions, sensor frames, calibration and latency. Records should distinguish raw readings from filtered or model-estimated states, and commands from measured responses.
Simulation adds another trap: exact base velocity, contact state or disturbance forces may be available to the trainer but not directly measurable on the robot. Marking these privileged signals prevents a deployable observation set from being confused with information used only during training.
Sources
Related terms
Hardware & control
Actuator
An actuator is a robot’s power mechanism that converts electrical, hydraulic, pneumatic or other supplied energy into controlled mechanical force, torque or motion. In a humanoid, actuators drive joints, often through transmissions; they are distinct from the joints and sensors, and their arrangement need not correspond one-to-one with the robot’s degrees of freedom.
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.
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.