Adaptive Character Motion Planning with Reinforcement Learning
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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 integrating a motion policy model trained with reinforcement learning and a discriminator to evaluate motion quality, allowing characters to navigate dynamic environments and interact naturally.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If physics-based motion planning techniques are used with static pre-planned paths, then manufacturing precision and path efficiency are improved, but adaptability to dynamic environmental changes deteriorates
Solution Approach 1:
The patent transitions from static pre-planned paths to dynamic motion generation using reinforcement learning. The system continuously adapts motions in real-time based on environmental feedback, allowing the character to respond to dynamic obstacles and terrain changes while maintaining natural movement patterns. This resolves the contradiction by making the motion planning system dynamic rather than static.
Solution Approach 2:
The reinforcement learning framework implements continuous feedback loops where the character's motions are evaluated against environmental states and rewards. This feedback mechanism enables real-time adaptation to environmental changes while maintaining precise control over movement quality, thus improving both adaptability and precision simultaneously.
2Productivity
If physics-based motion planning techniques prioritize path efficiency and robotic precision, then productivity and manufacturing precision are improved, but the naturalism and realism of movements deteriorates
Solution Approach 1:
The patent changes the optimization parameters from purely efficiency-based metrics to a composite reward function that includes naturalism, fluidity, and collision avoidance. This allows the system to generate motions that are both efficient and naturally realistic by adjusting the weightings of different reward components based on task requirements.
Solution Approach 2:
The patent replaces traditional physics-based mechanical motion planning with a reinforcement learning approach that learns natural movement patterns from data. This substitution enables the system to generate human-like motions without relying on complex physics simulations, improving both naturalism and computational efficiency.
3Device complexity
If static pre-planned paths are used for motion planning, then device complexity is reduced and ease of manufacture is improved, but adaptability to unforeseen environmental changes deteriorates
Solution Approach 1:
The reinforcement learning model enables the character to autonomously adapt its motions without external intervention or manual reprogramming. The system serves itself by learning from environmental feedback and automatically adjusting its behavior, reducing the need for complex manual path planning while improving adaptability.
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.