Robotics Data Pipeline for Synthetic Navigation World Models
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Solution Overview
Problem
Existing navigation systems for autonomous mobile robots (AMRs) face issues such as error propagation, lack of holistic understanding, significant re-engineering, and redundant processing, leading to reduced performance and difficulty in adapting to new environments and tasks.
Innovation Solution
A multimodal generative world model that integrates perception, planning, and control tasks using a single model to map sensory inputs to multimodal outputs, along with a data-generation pipeline for generating synthetic data to train and evaluate navigation systems across diverse environments.
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 throughout the system and holistic understanding is lost
Solution Approach 1:
The patent merges perception, planning, and control modules into a unified end-to-end navigation system. The combined model processes sensory inputs and generates navigation commands through integrated processing, eliminating error propagation between separate modules while maintaining holistic environmental understanding.
Solution Approach 2:
The unified navigation model performs multiple functions simultaneously - perception, planning, and control - within a single system architecture. This multi-functional approach allows the system to maintain holistic understanding while reducing error propagation, as all navigation tasks are coordinated through shared processing.
2Adaptability or versatility
If multiple separate navigation modules are used, then each module can have specialized parameters, but the system lacks holistic understanding and requires significant re-engineering for new tasks
Solution Approach 1:
The patent combines specialized navigation functions into a unified model that maintains adaptability through integrated processing. The end-to-end system learns task-specific behaviors while maintaining holistic understanding, reducing the need for re-engineering when adapting to new tasks or environments.
3Ease of manufacture
If multiple separate navigation modules are used, then each module can be independently optimized, but redundant processing increases latency and resource consumption
Solution Approach 1:
The patent merges navigation modules into a unified system that processes information in a single coordinated pass. This eliminates redundant processing and sequential delays between modules, reducing latency and improving real-time navigation performance while maintaining system optimization capabilities.
4Measurement precision
If manual data collection is performed for each new environment, then the robot can be trained for that specific environment, but the process is time-consuming and cannot be scaled
Solution Approach 1:
The patent uses synthetic data generation to create virtual copies of various environments for training the navigation system. This copying approach allows the robot to learn from simulated experiences across diverse environments without requiring time-consuming manual data collection for each specific location, enabling rapid adaptation and scaling.
Data Source
AI summary
In various examples, a technique for generating simulation data includes generating, via one or more simulations, simulation data associated with operation of a first machine in an environment. The technique also includes determining a command to the first machine based at least on the simulation data and a goal associated with the first machine and updating the simulation data based at least on the command. The technique further includes storing the simulation data, the command, and the updated simulation data in one or more data records, and causing a second machine to perform one or more actions based at least on the one or more data records.