Differentiable Motion Planning for Modular Autonomous Machines
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Modular architectures in autonomous machines, such as robotic systems and autonomous vehicles, face challenges with compounding errors, information bottlenecks, and integration issues, leading to reduced reusability, interpretability, and generalizability, which are critical for safety-critical applications.
Innovation Solution
Implementing a differentiable and modular prediction and planning system using analytical functions and neural networks that allow gradients to propagate backwards, enabling joint training of parameters across modules to enhance reusability and interpretability while reducing errors and bottlenecks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If modular architecture is used in autonomous machines, then reusability and interpretability are improved, but compounding errors and information bottlenecks occur between modules
Solution Approach 1:
The patent introduces a differentiable interface layer between modular components that acts as an intermediary to enable gradient flow. This interface includes differentiable sampling operations and differentiable search procedures that mediate between discrete module outputs and continuous optimization requirements, allowing error gradients to propagate through otherwise discontinuous module boundaries.
Solution Approach 2:
The patent transforms discrete, non-differentiable operations into continuous, differentiable parameterized operations. By representing module outputs as continuous probability distributions and using differentiable sampling techniques, the system enables parameter optimization through gradient descent while maintaining modular architecture benefits.
2Adaptability or versatility
If modular architecture is used in autonomous machines, then interpretability is improved, but integration challenges occur between modules
Solution Approach 1:
The differentiable interface serves as a mediator that unifies module integration through a common optimization framework. By providing a standardized differentiable interface, the patent simplifies integration complexity while preserving the interpretability of individual modular components through their parameterized representations.
3Productivity
If end-to-end neural networks are used, then information bottlenecks and performance scaling are improved, but reusability and interpretability are significantly lower
Solution Approach 1:
The patent segments the end-to-end neural network into distinct functional modules (prediction module, planning module, control module) while maintaining end-to-end differentiability. Each module has a specific interpretability function, and the differentiable interface allows gradient flow across module boundaries, combining the performance scaling of end-to-end networks with the interpretability of modular architectures.
4Loss of information
If end-to-end neural networks are used, then information bottlenecks are removed, but reusability is significantly lower
Solution Approach 1:
The patent divides the system into reusable modular components that can be independently developed and tested, then integrated through differentiable interfaces. This segmentation enables reusability while the end-to-end differentiable training removes information bottlenecks by allowing gradient flow across all modules during joint optimization.
Data Source
AI summary
In various examples, a motion planner include an analytical function to predict motion plans for a machine based on predicted trajectories of actors in an environment, where the predictions are differentiable with respect to parameters of a neural network of a motion predictor used to predict the trajectories. The analytical function may be used to determine candidate trajectories for the machine based on a predicted trajectory, to compute cost values for the candidate trajectories, and to select a reference trajectory from the candidate trajectories. For differentiability, a term of the analytical function may correspond to the predicted trajectory. A motion controller may use the reference trajectory to predict a control sequence for the machine using an analytical function trained to generate predictions that are differentiable with respect to at least one parameter of the analytical function used to compute the cost values.