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.
CITATION STYLE
Otte, S., Stoll, J., & Butz, M. V. (2019). Incorporating Adaptive RNN-Based Action Inference and Sensory Perception. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11730 LNCS, pp. 543–555). Springer Verlag. https://doi.org/10.1007/978-3-030-30490-4_44
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