Motivational Cognitive Maps Allow Robot Biomimetic Autonomy

The mammalian hippocampal formation plays a critical role in efficient and flexible navigation. Hippocampal place cells exhibit spatial tuning, characterized by increased firing rates when an animal occupies specific locations in its environment. The mechanisms underlying the encoding of spatial information by hippocampal place cells remain not fully resolved. Evidence suggests that spatial preferences are shaped by multimodal sensory inputs. Yet, existing hippocampal-inspired models typically rely on a single sensory information source. Here, we developed a hippocampus-inspired model that combines motivational and spatial encoding and is based on the fundamental principle of biological autonomy that behavior serves a purpose. That is, in foraging tasks, an agent’s trajectories must be deployed considering the fact that the reward value of environmental stimuli is tied to the agent’s motivational state. In this paper, we introduce a "motivational hippocampal autoencoder" (MoHA) that integrates both interoceptive (motivational) and exteroceptive (visual) information. The MoHA model reproduces hippocampal firing correlates for different motivational states. We show that the representations of MoHA allow a synthetic agent to learn and deploy efficient trajectories in a foraging task, laying the foundation for self-regulated multipurpose reinforcement learning.

Rosado, O. G., Amil, A. F., Freire, I. T., Vinck, M., & Verschure, P. F. (2025, October). Motivational Cognitive Maps Allow Robot Biomimetic Autonomy. In 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 14402-14408). IEEE.