5 accepted papers
In this paper, we present a low-cost, easy-to-implement sim-to-real framework for biped locomotion that narrows the reality gap using only simulation data, without motion-capture or additional real-world measurements. First, a walking policy for the BRUCE robot is trained in Isaac Gym via reinforcem…
In this paper, we present a low-cost, easy-to-implement sim-to-real framework for biped locomotion that narrows the reality gap using only simulation data, without motion-capture or additional real-world measurements. First, a walking policy for the BRUCE robot is trained in Isaac Gym via reinforcem
Capturability characterizes a safe region of states for humanoid walking and is most commonly constructed by analyzing the one-dimensional divergent component of motion (DCM) of the center of mass. In this work, by exploiting the mathematical structure of the step-to-step (S2S) dynamics, we characte
A quantum neural network (QNN) is a parameterized mapping efficiently implementable on near-term Noisy Intermediate-Scale Quantum (NISQ) computers. It can be used for supervised learning when combined with classical gradient-based optimizers. Despite the existing empirical and theoretical investigat…
EEG-based emotion recognition is an effective way to infer the inner emotional state of human beings. Recently, deep learning methods, particularly long short-term memory recurrent neural networks (LSTM-RNNs), have made encouraging progress for in the field of emotion recognition. However, the LSTM-…