Jul 12, 2021 · We tackle the problem of generalization to unseen configurations for dynamic tasks in the real world while learning from high-dimensional image input.
• We propose Hierarchical Neural Dynamic Policies (H-. NDPs) that embed the structure of dynamical systems in a hierarchical framework for end-to-end policy ...
H-NDPs are address the problem of generalization to unseen configurations for dynamic tasks in the real world while learning from high-dimensional image ...
Sep 12, 2024 · This work attempts to develop a hierarchical processing system capable of handling GPR data characterized by high degree of uncertainty, natural ...
... Within hierarchical RL, there also exists a class of compositional methods in which a high-level policy issues commands to be executed by a low-level policy ...
We propose. Neural Dynamic Policies (NDPs) that make predictions in trajectory distribution space as opposed to prior policy learning methods where action ...
We propose Neural Dynamic Policies (NDPs) that make predictions in trajectory distribution space as opposed to prior policy learning methods.
Missing: Hierarchical | Show results with:Hierarchical
We propose Neural Dynamic Policies (NDPs) that make predictions in trajectory distribution space as opposed to prior policy learning methods where actions ...
Dec 4, 2020 · This work introduces Autonomous Neural Dynamic Policies (ANDPs) that are based on autonomous dynamical systems, and are more flexible than traditional stable ...
Abstract. We present hierarchical policy blending as optimal transport (HiPBOT). HiPBOT hierarchically adjusts the weights of low-level reactive expert ...