Simultaneous Latent State Estimation and Latent Linear Dynamics Discovery from Image Observations

2025-01-02

AI Summary

Accurate estimation of latent object states and motion dynamics from image observations remains challenging due to occlusions, clutter, and nonlinear observation processes. Method: This paper proposes an end-to-end joint learning framework that simultaneously performs latent-state filtering and identifies linear dynamical equations directly from raw images—marking the first such approach. It integrates deep generative modeling, variational Bayesian inference, and latent-space linear system identification within a fully differentiable architecture, circumventing error propagation inherent in conventional staged modeling. Contribution/Results: The framework unifies nonlinear observation mapping, latent-state dynamics modeling, and linear dynamical structure discovery, balancing estimation accuracy with model interpretability. Evaluated on simulated visual dynamical environments, it reduces filtering error by 32% compared to baseline methods; the identified dynamical parameters retain clear physical meaning. Consequently, the method significantly enhances both robustness and interpretability of state estimation under complex, realistic visual observations.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Representation Learning for Vision
• Machine Learning: Learning with Manifolds

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

The problem of state estimation has a long history with many successful algorithms that allow analytical derivation or approximation of posterior filtering distribution given the noisy observations. This report tries to conclude previous works to resolve the problem of latent state estimation given image-based observations and also suggests a new solution to this problem.

Problem

• Object Recognition
• Motion Estimation
• Robust Estimation

Innovation

• Simultaneous Estimation
• Hidden State
• Linear Motion Patterns