  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) and non-visual observations (e.g., attitude
estimations). This confers the agent upon awareness not only of the scenario,
but also of its own state. In addition, we incorporate a termination likelihood
predictor model as an auxiliary loss function of the control policy, which
enables the agent to anticipate terminal states of success and failure. In this
manner, the sample efficiency of the approach for episodic tasks is increased.
Our model is evaluated on the NimbRo-OP2X humanoid robot that navigates in
scenes avoiding collisions efficiently in simulation and with the real
hardware.
