Latent Representation for Robot Navigation Without 3D Reconstruction
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Solution Overview
Problem
Existing navigation systems for robots and vehicles face challenges in efficiently navigating through environments without visual observations, particularly in avoiding obstacles and finding navigable paths, as they rely heavily on explicit reconstruction of 3D spaces which may not be useful for downstream tasks and can harm transferability from simulation to real-world scenarios.
Innovation Solution
A learning system comprising a navigation module with a first policy that determines actions based on camera images and a second policy that navigates using a latent representation of the environment, without visual observations, combined with a representation module that selectively learns and updates the representation using neural networks, including GRU or LSTM, to optimize navigability and avoid obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If explicit reconstruction of 3D spaces is used for navigation, then obstacle avoidance and path finding capabilities are improved, but the system becomes more complex and less transferable from simulation to real-world scenarios
Solution Approach 1:
The patent extracts only the necessary navigation-related information from the full 3D scene reconstruction, separating the useful navigational features from the redundant visual details. This allows the system to maintain obstacle avoidance capability while reducing computational complexity by focusing only on essential spatial relationships and navigable areas rather than complete 3D modeling.
Solution Approach 2:
The patent creates a simplified navigable space representation that copies only the essential navigation properties from the real environment, rather than replicating the complete 3D structure. This abstract representation retains obstacle information and path feasibility while eliminating the need for complex explicit reconstruction, thereby reducing system complexity.
2Reliability
If explicit reconstruction of 3D spaces is used for navigation, then obstacle avoidance and path finding capabilities are improved, but transferability from simulation to real-world scenarios deteriorates
Solution Approach 1:
The patent changes the representation parameters from detailed 3D geometric models to a simplified navigable space graph that captures only essential navigation properties. This parameter transformation makes the system more adaptable across different environments and robust to simulation-to-real transfer, as the abstract representation focuses on functional navigability rather than specific geometric details that may vary between domains.
3Device complexity
If latent representation learning is used instead of explicit reconstruction, then system complexity is reduced and transferability is improved, but the quality of spatial representation may deteriorate
Solution Approach 1:
The patent incorporates feedback mechanisms where the navigable space representation is continuously refined based on navigation success and failure observations. This feedback loop ensures that even though the representation is latent and simplified, it maintains sufficient spatial accuracy and navigational precision by adapting to actual navigation outcomes, thereby compensating for the inherent abstraction.
Data Source
AI summary
A learning system for a navigating robot includes: a navigation module including: a first policy configured to determine actions for moving the navigating robot and navigating from a starting location to an ending location based on images from a camera of the navigating robot; and a second policy configured to, based on a representation of an environment generated by the navigating robot, determine actions for moving the navigating robot from waypoint locations between the starting location and the ending location to a plurality of subgoal locations without any images from the camera; and a representation module configured to: selectively learn the representation during movement via the first policy based on the representation at previous times, images from the camera, and actions determined by the first policy at previous times; and provide the representation to the second policy.