Robotics

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.

We propose to create autonomous interactive robots, by training a model to imitate operator data.

We introduce a novel method that integrates kinematic generative models with physics based character control. Our approach begins by training a reward surrogate to predict the performance of the downstream non-differentiable control task, offering an efficient and differentiable loss function.

We introduce a new bipedal robot, designed with a focus on character-driven mechanical features.

We present a pipeline for fusing text and audio features to extract and re-use user information like names with the correct pronunciation.