Bayesian Object Models for Efficient Structure and Dynamics Prediction
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Solution Overview
Problem
Existing methods for modeling objects in complex environments require significant computing resources, leading to inefficiencies in memory and time usage.
Innovation Solution
A Bayesian object model (BOM) that utilizes a differentiable probabilistic program to encode structural and kinodynamic attributes of objects, enabling efficient and automated Bayesian inference through gradient-based updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to model objects in complex environments, then modeling accuracy is maintained, but computing resources, memory, and time consumption increase significantly
Solution Approach 1:
The patent segments the object modeling process into distinct components: structure attributes, kinodynamic attributes, and observational attributes. Each component is modeled separately using probabilistic programs, allowing independent optimization and reducing overall computational complexity while maintaining comprehensive modeling accuracy.
Solution Approach 2:
The patent transforms the modeling approach by changing parameters from deterministic values to probabilistic distributions. This allows the system to represent uncertainty and variability in object attributes, improving robustness while enabling more efficient inference through gradient-based optimization methods.
2Measurement precision
If detailed object modeling is performed, then prediction accuracy improves, but memory usage and processing time increase
Solution Approach 1:
The patent performs preliminary actions by pre-defining probabilistic programs for structure and kinodynamic attributes. These programs are prepared in advance with gradient computation rules, enabling fast inference during actual prediction tasks without requiring complex real-time calculations.
Solution Approach 2:
The patent substitutes traditional mechanical inference methods with gradient-based probabilistic inference. By using differentiable probabilistic programs and gradient descent optimization, the system achieves efficient computation of complex probabilistic models, replacing computationally intensive sampling methods with faster gradient-based approaches.
Data Source
AI summary
Apparatuses, systems, and techniques to update a machine learning model associated with an object. In at least one embodiment, the machine learning model is updated based at least in part on, for example, one or more distributions associated with the machine learning model.