Recent advances in neural networks have enabled agents to perform high-level reasoning in complex environments, where robust environmental understanding must often be obtained from accumulated observations rather than from fixed scene labels. Existing spatial abstractions commonly rely on scene-level representations, object-presence cues, scene graphs, or language-grounded category prompts; depending on the task, these abstractions can either discard distributional object evidence or introduce additional structure that is not directly grounded in the agent’s own experience. To address this information abstraction imbalance, we design an object-centric spatial abstraction (OSA) that models environments as empirical distributions over object embeddings. Building on this abstraction, we present PEU-OSA, a Probabilistic Environment Understanding framework via Object-centric Spatial Abstraction. PEU-OSA comprises three conceptual modules: an object perception module, the OSA module, and a memory component. Scene observations are transformed into object representations, abstracted into empirical distributions through OSA, and accumulated in memory for comparison with new observations. The OSA module captures three fundamental relationships in the latent space via kernel density estimation: object-object, object-environment, and environment-environment relationships. To characterize when these measurements are reliable, we propose the ( )-Statistically Separable (EDS) function, which quantifies the separability and concentration of object representations and provides theoretical conditions and empirical evidence for OSA estimates. We evaluate PEU-OSA through EDS-based analysis on ImageNet, scene classification on Places, chained inference in Replica, and applicability studies for embodied agents in Replica and Minecraft. In a Replica chained-inference setting designed to test generalization to unseen environments, PEU-OSA improves top-1 retrieval accuracy from 0.36 with CLIP-based vision-language retrieval to 0.78, suggesting that object-distribution memory can provide a useful grounding source when scene-level language retrieval is insufficient. These findings support PEU-OSA as an annotation-free, experience-grounded framework for probabilistic environment understanding across recognition, reasoning, and embodied memory scenarios.

Recent machine learning algorithms have been developed using well-curated datasets, which often require substantial cost and resources. On the other hand, the direct use of raw data often leads to overfitting towards frequently occurring class information. To address class imbalances cost-efficiently, we propose an active data filtering process during self-supervised pre-training in our novel framework, Duplicate Elimination (DUEL). This framework integrates an active memory inspired by human working memory and introduces distinctiveness information, which measures the diversity of the data in the memory, to optimize both the feature extractor and the memory. The DUEL policy, which replaces the most duplicated data with new samples, aims to enhance the distinctiveness information in the memory and thereby mitigate class imbalances. We validate the effectiveness of the DUEL framework in class-imbalanced environments, demonstrating its robustness and providing reliable results in downstream tasks. We also analyze the role of the DUEL policy in the training process through various metrics and visualizations.

Toddlers evolve from free exploration with sparse feedback to exploiting prior experiences for goal-directed learning with denser rewards. Drawing inspiration from this Toddler-Inspired Reward Transition, we set out to explore the implications of varying reward transitions when incorporated into Reinforcement Learning (RL) tasks. Central to our inquiry is the transition from sparse to potential-based dense rewards, which share optimal strategies regardless of reward changes. Through various experiments, including those in egocentric navigation and robotic arm manipulation tasks, we found that proper reward transitions significantly influence sample efficiency and success rates. Of particular note is the efficacy of the toddler-inspired Sparse-to-Dense (S2D) transition. Beyond these performance metrics, using Cross-Density Visualizer technique, we observed that transitions, especially the S2D, smooth the policy loss landscape, promoting wide minima that enhance generalization in RL models.

Understanding geometric concepts, such as distance and shape, is essential for understanding the real world and also for many vision tasks. To incorporate such information into a visual representation of a scene, we propose learning to represent the scene by sketching, inspired by human behavior. Our method, coined Learning by Sketching (LBS), learns to convert an image into a set of colored strokes that explicitly incorporate the geometric information of the scene in a single inference step without requiring a sketch dataset. A sketch is then generated from the strokes where CLIP-based perceptual loss maintains a semantic similarity between the sketch and the image. We show theoretically that sketching is equivariant with respect to arbitrary affine transformations and thus provably preserves geometric information. Experimental results show that LBS substantially improves the performance of object attribute classification on the unlabeled CLEVR dataset, domain transfer between CLEVR and STL-10 datasets, and for diverse downstream tasks, confirming that LBS provides rich geometric information.

Active learning is widely used to reduce labeling effort and training time by repeatedly querying only the most beneficial samples from unlabeled data. In real-world problems where data cannot be stored indefinitely due to limited storage or privacy issues, the query selection and the model update should be performed as soon as a new data sample is observed. Various online active learning methods have been studied to deal with these challenges; however, there are difficulties in selecting representative query samples and updating the model efficiently without forgetting. In this study, we propose Message Passing Adaptive Resonance Theory (MPART) that learns the distribution and topology of input data online. Through message passing on the topological graph, MPART actively queries informative and representative samples, and continuously improves the classification performance using both labeled and unlabeled data. We evaluate our model in stream-based selective sampling scenarios with comparable query selection strategies, showing that MPART significantly outperforms competitive models.

Semi-supervised learning and continuous learning are fundamental paradigms for human-level intelligence. To deal with real-world problems where labels are rarely given and the opportunity to access the same data is limited, it is necessary to apply these two paradigms in a joined fashion. In this paper, we propose Label Propagation Adaptive Resonance Theory (LPART) for semi-supervised continuous learning. LPART uses an online label propagation mechanism to perform classification and gradually improves its accuracy as the observed data accumulates. We evaluated the proposed model on visual (MNIST, SVHN, CIFAR-10) and audio (NSynth) datasets by adjusting the ratio of the labeled and unlabeled data. The accuracies are much higher when both labeled and unlabeled data are used, demonstrating the significant advantage of LPART in environments where the data labels are scarce.