The study investigates how intrinsic motivation drives autonomous behavior in fruit flies by combining whole-brain recordings with a neuromechanical digital twin. It shows that a hierarchical policy selecting from low‑level motor primitives, trained with 20 intrinsic motivation algorithms, can reproduce fly locomotor statistics and internal representations of future actions. The DIAYN algorithm, which maximizes behavioral entropy and sensory separability, best matches fly behavior and neural coding, capturing individual variability and interhemispheric kinematic encoding.
Animals have a remarkable capacity for autonomy---driven by intrinsic motivation---without external sensory cues or extrinsic objectives, yet the computational mechanisms behind this ability largely r...
arXiv:2609.17325v1 Announce Type: new Abstract: Biological cells can be viewed as individual, interacting agents whose collective dynamics give rise to adaptive behaviour at multiple levels of organi...
arXiv:2602. 17997v3 Announce Type: replace Abstract: Animals perform coordinated whole-body movements under the control of neural systems shaped by brain-wide connectivity.
arXiv:2606. 20858v2 Announce Type: replace Abstract: The temporal structure of reward composition in reinforcement learning (RL) is typically hand-designed and held fixed throughout training, leaving the progression of motivational priorities largely unexplored.
The article introduces a mathematical model of the Motivated Emotional Mind cognitive architecture for embodied intelligent systems. It formalizes a re‑entrant loop that integrates feedforward processing, lateral interactions, and feedback pathways, along with representational selection mechanisms that govern adaptive responses. The model binds exteroceptive and interoceptive signals, bodily-motivational context, and memory traces into associative structures called semblions, enabling motivated learning that incorporates need thresholds, goal generation, and regulatory constraints.
arXiv:2610.02196v1 Announce Type: cross Abstract: We study test-time evolution for humanoid loco-manipulation: solving tasks that a controller was never trained for by repurposing its existing skills...
The paper introduces SCAMPER, a system that trains a virtual rat model to exhibit realistic movements during both goal-directed tasks and random exploration. SCAMPER employs a variational architecture with two priors—a motion prior for control and a behavior prior for exploration—to generate natural behavior and effectively exploit task structure. The system can learn from sparse rewards, enabling realistic modeling of sparse-reward tasks used in experimental neuroscience.
arXiv:2607. 13560v1 Announce Type: cross Abstract: Recent advances in generative and embodied AI have been driven by large-scale predictive learning over multimodal data.
The paper introduces a mathematical model of the Motivated Emotional Mind cognitive architecture for embodied intelligent systems, describing how the system learns to maintain homeostasis via motivated learning—a reinforcement‑learning variant driven by internal motivations. It formalizes a re‑entrant loop that integrates feedforward processing, lateral interactions, and feedback pathways, and details how exteroceptive and interoceptive signals, bodily context, and memory traces form associative structures called semblions that compete for processing and reconstruction. The model incorporates need thresholds, goal dynamics, bodily state, resource constraints, and action uncertainty, and posits global affect as a central control signal modulating learning rate, representational valence, and exploration‑exploitation balance.
arXiv:2605.13775v2 Announce Type: replace-cross Abstract: The scalability of robotic manipulation is fundamentally bottlenecked by the scarcity of task-aligned physical interaction data. While vision...
The paper introduces Hamiltonian Temporal Contrastive Curiosity (HTC²), a method that combines multi‑horizon temporal representation learning with Hamiltonian phase‑space structure to guide exploration in physical control environments. HTC² uses a horizon‑conditioned contrastive critic to learn relationships between future state pairs within an episode, and the resulting representation distance serves as an intrinsic reward. The reward is further modulated by bounded multi‑step Hamiltonian variation, and the framework is integrated with Soft Actor‑Critic and Proximal Policy Optimization, evaluated on six DeepMind Control Suite tasks against several baseline curiosity methods.
Exploration in physical control environments requires agents to identify actions whose consequences reveal previously unexplored aspects of the underlying dynamics. However, many intrinsic motivation...