We learn latent representations enforced with known transformation laws (group action), and apply this idea on equivariant latent world modeling.
We present a deep imitation learning framework for robotic bimanual manipulation in a continuous state-action space.
We propose a parameterized action reinforcement learning algorithm to improve the performance of match plan generation in Bing search.