SlotPi: Physics-informed Object-centric Reasoning Models (2506.10778v1)

Abstract: Understanding and reasoning about dynamics governed by physical laws through visual observation, akin to human capabilities in the real world, poses significant challenges. Currently, object-centric dynamic simulation methods, which emulate human behavior, have achieved notable progress but overlook two critical aspects: 1) the integration of physical knowledge into models. Humans gain physical insights by observing the world and apply this knowledge to accurately reason about various dynamic scenarios; 2) the validation of model adaptability across diverse scenarios. Real-world dynamics, especially those involving fluids and objects, demand models that not only capture object interactions but also simulate fluid flow characteristics. To address these gaps, we introduce SlotPi, a slot-based physics-informed object-centric reasoning model. SlotPi integrates a physical module based on Hamiltonian principles with a spatio-temporal prediction module for dynamic forecasting. Our experiments highlight the model's strengths in tasks such as prediction and Visual Question Answering (VQA) on benchmark and fluid datasets. Furthermore, we have created a real-world dataset encompassing object interactions, fluid dynamics, and fluid-object interactions, on which we validated our model's capabilities. The model's robust performance across all datasets underscores its strong adaptability, laying a foundation for developing more advanced world models.

• The paper introduces a hybrid framework, SlotPi, that integrates a Hamiltonian-based physics module to provide essential constraints in object-centric reasoning.
• It combines a spatiotemporal reasoning module to capture non-conservative dynamics, enhancing prediction accuracy in complex real-world scenarios.
• Empirical evaluations on CLEVRER and NS fluid datasets demonstrate that SlotPi outperforms traditional models with improved metrics in dynamic simulation tasks.

Analysis of "SlotPi: Physics-informed Object-centric Reasoning Models"

This paper introduces "SlotPi," a novel reasoning framework that integrates physics-informed principles within object-centric models for dynamic prediction tasks. SlotPi is designed to enhance the predictive capabilities of models by incorporating insights from physical laws. The primary aim is to address the inadequacies in existing object-centric dynamic simulation methods by embedding a physical understanding of the environment directly into the computational framework, which is articulated through a Hamiltonian-based physics module and an advanced spatiotemporal reasoning module.

Core Contributions

1. Physics Module Integration: The paper introduces a physics module derived from Hamiltonian principles, offering physical constraints necessary for accurate reasoning. This module calculates generalized momentum and coordinates of slot representations through cross-attention and self-attention mechanisms, achieving an overview between computational inference and physical dynamics.
2. Spatiotemporal Reasoning: The research recognizes the limitations of purely Hamiltonian models when applied in non-conservative systems typical of real-world applications. To counteract these limitations, the spatiotemporal reasoning module is designed to capture dynamics not readily inferred by the physics module, thereby enriching the model's predictive robustness.
3. Dataset Creation and Evaluation: A significant advancement in this research is the construction of a comprehensive real-world dataset. This dataset includes interactions involving fluids and objects, providing a rigorous benchmark for evaluating the model's adaptability and reliability in multifaceted environments.

Empirical Validation

SlotPi shows notable improvements across various datasets, which include intricate object dynamics (CLEVRER), scenarios with fluid dynamics (NS fluid dataset), and real-world interactions involving fluids and objects. In the CLEVRER dataset experiments, SlotPi demonstrates superior performance in terms of object dynamic predictions as measured by FG-ARI and FG-mIoU metrics, suggesting enhanced segment consistency and accuracy under complex scene interactions.

In predictive tasks involving the NS fluid dataset, SlotPi's results surpass those of traditional models like Fourier Neural Operator (FNO) and UNet in root mean square error (RMSE), mean absolute error (MAE), and high-correlation time (HCT). This suggests that the SlotPi framework is not only effective in handling rigid body simulations but can also extend its capabilities to predict fluid dynamics accurately.

Implications and Future Directions

The implications of integrating domain-specific knowledge, such as principles from classical mechanics, into object-centric modeling are profound. Such integration can serve as an essential tool in advancing our understanding of dynamical systems within Artificial Intelligence. Moving forward, exploring the application of SlotPi across broader domains with more heterogeneous datasets will be critical. Furthermore, advancements could include the integration of learning frameworks that unify training across multiple types of interactions simultaneously, mitigating the current requirement for dataset-specific retraining.

The SlotPi framework holds potential for extending into domains where reasoning about physical interactions is crucial, such as robotics, autonomous vehicle navigation, and augmented reality applications. By facilitating accurate modeling of environments that include both object and fluid dynamics, SlotPi is poised to contribute significantly to the field of real-time simulation and complex systems modeling.

In conclusion, the SlotPi model rigorously combines object-centric reasoning with embedded physical laws for enhanced predictive accuracy over complex, dynamic scenarios, illustrating noteworthy advancements in the operationalization of intuitive physics within AI systems.