System identification is an essential first step in robotic control. Here we focus on the calibration of kinematic sensors, such as joint angle potentiometers, tendon/actuator extension sensors and motion capture markers, on complex humanoid robots. Manual calibration with protractors and rulers does not scale to complex humanoids like the ones studied here. Classic automatic approaches cross-calibrate multiple sensor systems on the same robot by exploiting their redundancy. However, these

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... aches make the strong assumption that the observed joint angles are functions of the sensor measurements plus observation noise. This assumption is too restrictive on modern humanoids where linear actuators and tendons span multiple joints. Here we formulate the calibration problem as a Bayesian inference process on a generative model where hidden joint-angles generate sensor observations. A novel alternating optimization approach is developed to simultaneously track space-time joint angles and calibrate parameters (STAC). Explicit estimation of joint angles makes it possible to calibrate sensors that otherwise cannot be handled by classical approaches, such as tendons wrapping on complicated surfaces and spanning multiple joints. We evaluate STAC to calibrate joint potentiometer, tendon length sensor and motion capture marker positions, on a 38-DoF humanoid robot with 24 optical markers, and a 24 DoF tendon driven hand with 12 markers. We show that STAC can be applied to problems that cannot be handled with classical approaches. In addition we show that for simpler problems STAC is more robust than classical approaches and other probabilistic approaches such as the Extended Kalman Filter.