Yaru Gu

Enhancing Leg Odometry in Legged Robots with Learned Contact Bias: An LSTM Recurrent Neural Network Approach

To address the leg odometry drift caused by the non-stationary foot contact, this paper introduces a novel data-driven based leg odometry technique for legged robots. By leveraging a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN), the method learns the biases in the robot’s foot contac…