Mapless Humanoid Navigation Using Learned Latent Dynamics
André Brandenburger,Diego Rodriguez,Sven Behnke,André Brandenburger,Diego Rodriguez,Sven Behnke
In this paper, we propose a novel Deep Reinforcement Learning approach to address the mapless navigation problem, in which the locomotion actions of a humanoid robot are taken online based on the knowledge encoded in learned models. Planning happens by generating open-loop trajectories in a learned latent space that captures the dynamics of the environment. Our planner considers visual (RGB images...
