Lookahead Planning Gradient for Virtual Agent Motion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Motion planning for virtual agents becomes increasingly resource-intensive and computationally wasteful as the planning horizon extends into the future, leading to inefficiencies in resource consumption.
Innovation Solution
The implementation of a lookahead planning gradient system that generates a search-tree with varying granularity of physical motion plans based on temporal distance, optimizing computational resources by using high resolution for immediate tasks, medium resolution for intermediate tasks, and low resolution for distant tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If motion planning is performed for distant future tasks with high resolution, then task execution precision is improved, but computational resource consumption increases significantly
Solution Approach 1:
The patent applies local quality by assigning different resolution levels to different temporal regions of the motion planning horizon. Immediate tasks (near future) are planned with high resolution to ensure precision, while distant future tasks are planned with low resolution to conserve computational resources. This creates a spatial-temporal gradient where planning granularity varies locally based on temporal distance from the current state.
Solution Approach 2:
The patent implements dynamics by making the planning resolution adaptive and variable rather than static. The resolution level dynamically adjusts based on the temporal distance of task nodes from the current state, allowing the system to optimize between precision and computational cost in real-time as the virtual agent progresses through its task sequence.
2Duration of action of moving object
If motion planning horizon is extended further into the future, then long-term task coordination is improved, but computational waste increases for speculative planning
Solution Approach 1:
The patent applies local quality by differentiating between near-future and far-future task nodes in the planning horizon. Immediate tasks receive full computational attention with high-resolution planning, while distant tasks receive minimal planning effort with low resolution. This localized approach allows the system to maintain a long planning horizon for coordination purposes without uniformly applying high computational cost across all time points.
Solution Approach 2:
The patent implements partial action by performing incomplete or low-resolution motion planning for distant future tasks. Rather than fully planning every detail of distant tasks, the system performs partial planning that provides rough guidance while deferring detailed planning to when those tasks become more immediate, thus reducing computational waste on speculative distant planning.
Data Source
AI summary
In one implementation, a method for improved motion planning. The method includes: obtaining a macro task for a virtual agent within a virtual environment; generating a search-tree based on at least one of the macro task, a state of the virtual environment, and a state of the virtual agent, wherein the search-tree includes a plurality of task nodes corresponding to potential tasks for performance by the virtual agent in furtherance of the macro task; and determining physical motion plans (PMPs) for at least some of the plurality of task nodes within the search-tree in order to generate a lookahead planning gradient for the first time, wherein a granularity of a PMP for a respective task node in the first search-tree is a function of the temporal distance of the respective task node from the first time.