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This article approaches the problem of controlling quadrupedal running and jumping motions with a parameterized, model-based, state-feedback controller. Inspired by the motor learning principles observed in nature, our method automatically fine tunes the parameters of our controller by repeatedly executing slight variations of the same motion task. This learn-through-practice process is performed...
This paper presents the vision of the recently launched project AGILITY that aims to join forces across Europe to bring robots with legs and arms into highly unstructured outdoor environment such as a disaster area. Building upon state of the art torque controllable quadrupedal robots, we jointly investigate environment perception, motion planning, and whole body control strategies that enable rough...
This paper introduces a novel batch optimization based calibration framework for legged robots. Given a non-degenerate calibration dataset and considering the stochastic models of the sensors, the task is formulated as a maximum likelihood problem. In order to facilitate the derivation of consistent measurement equations, the trajectory of the robot and other auxiliary variables are included into...
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