CoCo-InEKF: State Estimation with Learned Contact Covariances in Dynamic, Contact-Rich Scenarios
Robust state estimation for highly dynamic motion of legged robots remains challenging, especially in dynamic, contact-rich scenarios. Traditional approaches often rely on binary contact states that fail to capture the nuances of partial contact or directional slippage. This paper presents CoCo-InEKF, a differentiable invariant extended Kalman filter that utilizes continuous contact velocity covariances instead of binary contact states.
Josefine Klintberg
Dario Mylonopoulos
Vassilios Tsounis
Design and Control of a Bipedal Robotic Character
We introduce a new bipedal robot, designed with a focus on character-driven mechanical features.
David Muelller
Erika Varis Doggett
An Automatic Evaluation Framework for Social Conversations with Robots
Reshma Kantharaju
Evan Harber
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