We formulate how differentiable planning algorithms can exploit inherent symmetry in path planning problems, named SymPlan, and propose practical algorithms.
We study how implicit differentiation helps scale up and improve convergence of differentiable planning algorithms.
We formulate compositional generalization in object-oriented world modeling, and propose a soft and efficient mechanism for practice.
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.