Incorporating Adaptive RNN-Based Action Inference and Sensory Perception

Abstract

In this paper we investigate how directional distance signals can be incorporated in RNN-based adaptive goal-direction behavior inference mechanisms, which is closely related to formalizations of active inference. It was shown previously that RNNs can be used to effectively infer goal-directed action control policies online. This is achieved by projecting hypothetical environmental interactions dependent on anticipated motor neural activities into the future, back-projecting the discrepancies between predicted and desired future states onto the motor neural activities. Here, we integrate distance signals surrounding a simulated robot flying in a 2D space into this active motor inference process. As a result, local obstacle avoidance emerges in a natural manner. We demonstrate in several experiments with static as well as dynamic obstacle constellations that a simulated flying robot controlled by our RNN-based procedure automatically avoids collisions, while pursuing goal-directed behavior. Moreover, we show that the flight direction dependent regulation of the sensory sensitivity facilitates fast and smooth traversals through tight maze-like environments. In conclusion, it appears that local and global objectives can be integrated seamlessly into RNN-based, model-predictive active inference processes, as long as the objectives do not yield competing gradients.