Generative World Model Navigation for Holistic Robot Control
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Solution Overview
Problem
Existing navigation systems for autonomous mobile robots (AMRs) face issues such as error propagation across modules, lack of holistic understanding, significant re-engineering for new tasks/environments, and redundant processing, leading to reduced performance and inefficiency in real-time applications.
Innovation Solution
A multimodal generative world model that integrates perception, planning, and control into a single model, using encoders and decoders to convert inputs into embedded outputs for navigation and task performance, with a data-generation pipeline to create synthetic datasets for training and evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If multiple separate navigation modules are used for perception, planning, and control, then each module can be independently designed and optimized, but errors propagate across modules and the system lacks holistic understanding leading to reduced navigation performance
Solution Approach 1:
The patent merges perception, planning, and control into a single integrated navigation module that processes sensor data and generates control commands in one unified system. This eliminates error propagation between separate modules and enables holistic environmental understanding through shared internal representations, directly resolving the contradiction between independent design ease and navigation reliability.
Solution Approach 2:
The integrated navigation module performs multiple functions (perception, planning, control) within a single system architecture. The module uses unified internal representations to handle diverse tasks including navigation, obstacle avoidance, and task execution, achieving multi-functionality without requiring separate specialized modules for each function.
2Adaptability or versatility
If multiple separate navigation modules are used with distinct design parameters, then each module can be optimized for its specific function, but the system lacks holistic understanding and requires significant re-engineering for new tasks and environments
Solution Approach 1:
By combining perception, planning, and control into one module with shared internal representations, the system achieves adaptability to new tasks and environments without requiring re-engineering of multiple separate modules. The unified architecture learns holistic environmental understanding that generalizes across different scenarios, reducing the complexity of adaptation while maintaining versatility.
3Measurement precision
If redundant and sequential processing is used across multiple modules, then each module can perform its specialized function thoroughly, but latency and resource consumption increase negatively impacting real-time performance
Solution Approach 1:
The integrated navigation module processes perception, planning, and control in a unified pipeline rather than through sequential separate modules. This eliminates redundant processing steps and reduces latency while maintaining processing accuracy through the module's comprehensive internal representations that capture essential environmental features needed for all navigation decisions.
4Reliability
If manual data collection is performed for each new environment, then the robot can be trained on specific environment data, but the process is time-consuming and the robot cannot generalize to other environments
Solution Approach 1:
The integrated navigation module performs preliminary learning of holistic environmental representations from training data, enabling the system to generalize to new environments without requiring manual data collection for each specific environment. The unified module learns transferable navigation skills that apply across diverse settings, eliminating the need for environment-specific data gathering while maintaining training accuracy.
Data Source
AI summary
In various examples, a technique for performing end-to-end navigation using a generative world model includes converting a set of sensory inputs received by a machine at a current time step into a set of embedded features. The technique also includes generating, via execution of one or more neural networks, one or more states associated with the current time step based at least on the set of embedded features, a history of states preceding the current time step, and a first set of actions associated with a previous time step. The technique further includes converting, via execution of the one or more neural networks, the one or more states into a set of predictions associated with the current time step, and performing, by the machine, a second set of actions associated with the current time step based on the set of predictions.