Monthly Archives: July 2025

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Including “fear” and “curiosity” in RL applied to robot navigation

D. Hu, L. Mo, J. Wu and C. Huang, “Feariosity”-Guided Reinforcement Learning for Safe and Efficient Autonomous End-to-End Navigation, IEEE Robotics and Automation Letters, vol. 10, no. 8, pp. 7723-7730, Aug. 2025, 10.1109/LRA.2025.3577523.

End-to-end navigation strategies using reinforcement learning (RL) can improve the adaptability and autonomy of Autonomous ground vehicles (AGVs) in complex environments. However, RL still faces challenges in data efficiency and safety. Neuroscientific and psychological research shows that during exploration, the brain balances between fear and curiosity, a critical process for survival and adaptation in dangerous environments. Inspired by this scientific insight, we propose the “Feariosity” model, which integrates fear and curiosity model to simulate the complex psychological dynamics organisms experience during exploration. Based on this model, we developed an innovative policy constraint method that evaluates potential hazards and applies necessary safety constraints while encouraging exploration of unknown areas. Additionally, we designed a new experience replay mechanism that quantifies the threat and unknown level of data, optimizing their usage probability. Extensive experiments in both simulation and real-world scenarios demonstrate that the proposed method significantly improves data efficiency, asymptotic performance during training. Furthermore, it achieves higher success rates, driving efficiency, and robustness in deployment. This also highlights the key role of mimicking biological neural and psychological mechanisms in improving the safety and efficiency through RL.

It seems that human predictive brain works by predicting at the abstract level, not at the sensory level

Kaitlyn M. Gabhart, Yihan (Sophy) Xiong, André M. Bastos, Predictive coding: a more cognitive process than we thought?, Trends in Cognitive Sciences, Volume 29, Issue 7, 2025, Pages 627-640, 10.1016/j.tics.2025.01.012.

In predictive coding (PC), higher-order brain areas generate predictions that are sent to lower-order sensory areas. Top-down predictions are compared with bottom-up sensory data, and mismatches evoke prediction errors. In PC, the prediction errors are encoded in layer 2/3 pyramidal neurons of sensory cortex that feed forward. The PC model has been tested with multiple recording modalities using the global–local oddball paradigm. Consistent with PC, neuroimaging studies reported prediction error responses in sensory and higher-order areas. However, recent studies of neuronal spiking suggest that genuine prediction errors emerge in prefrontal cortex (PFC). This implies that predictive processing is a more cognitive than sensory-based mechanism – an observation that challenges PC and better aligns with a framework we call predictive routing (PR).

Evidences of the dimensionality reduction and augmentation performed by the brain

Casper Kerrén, Daniel Reznik, Christian F. Doeller, Benjamin J. Griffiths, Exploring the role of dimensionality transformation in episodic memory, Trends in Cognitive Sciences, Volume 29, Issue 7, 2025, Pages 614-626, 10.1016/j.tics.2025.01.007.

Episodic memory must accomplish two adversarial goals: encoding and storing a multitude of experiences without exceeding the finite neuronal structure of the brain, and recalling memories in vivid detail. Dimensionality reduction and expansion (‘dimensionality transformation’) enable the brain to meet these demands. Reduction compresses sensory input into simplified, storable codes, while expansion reconstructs vivid details. Although these processes are essential to memory, their neural mechanisms for episodic memory remain unclear. Drawing on recent insights from cognitive psychology, systems neuroscience, and neuroanatomy, we propose two accounts of how dimensionality transformation occurs in the brain: structurally (via corticohippocampal pathways) and functionally (through neural oscillations). By examining cross-species evidence, we highlight neural mechanisms that may support episodic memory and identify crucial questions for future research.