Character Motion Planning With Machine Learning for Dynamic Scenes
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Solution Overview
Problem
Conventional motion planning techniques for robots and virtual characters rely on static pre-planned paths, which fail to adapt in real-time to dynamic and unpredictable environments, leading to mechanical movements that lack realism and natural accuracy, especially in scenarios with unforeseen obstacles or terrain changes.
Innovation Solution
A computer-implemented method using a trained machine learning model to generate adaptive and realistic character motions by combining a motion policy model with a discriminator, employing reinforcement learning and Adversarial Motion Prior (AMP) to adjust to dynamic obstacles and terrain changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If physics-based motion planning with static pre-planned paths is used, then path efficiency and robotic precision are improved, but adaptability to dynamic environmental changes deteriorates
Solution Approach 1:
The system transitions from static pre-planned paths to dynamic motion generation using a trained machine learning model that processes real-time scene information and character state to generate adaptive motions. The model continuously updates motion plans based on environmental changes, obstacles, and terrain variations, enabling real-time adaptation while maintaining path efficiency through learned optimal trajectories.
2Productivity
If physics-based motion planning with static pre-planned paths is used, then path efficiency is improved, but motion realism and natural accuracy deteriorate
Solution Approach 1:
The system replaces traditional physics-based mechanical motion planning with a machine learning model trained on human motion data. The model generates motions that mimic natural human movement patterns while maintaining path efficiency, producing realistic and fluid character animations that avoid the mechanical appearance of conventional approaches.
3Adaptability or versatility
If manual recalibration or reprogramming of paths is performed, then adaptability to environmental changes is improved, but response time and operational efficiency deteriorate
Solution Approach 1:
The system implements self-service through an autonomous machine learning model that automatically generates and adjusts motion plans based on real-time scene information. The model processes environmental changes, obstacles, and terrain variations independently, enabling real-time adaptation without human intervention and eliminating time-consuming manual recalibration processes.
Data Source
AI summary
One embodiment of a method for controlling a character includes receiving a state of the character, a path to follow, and first information about a scene, generating, via a trained machine learning model and based on the state of the character, the path, and the first information, a first action for the character to perform, wherein the first action comprises a first type of motion included in a plurality of types of motions for which the trained machine learning model is trained to generate actions, and causing the character to perform the first action.