Multi-Modal Robot Hazard Mapping for Semantic Obstacle Avoidance
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Solution Overview
Problem
Existing hazard detection techniques for robots struggle to accurately identify and navigate around obstacles, particularly those with similar geometries, and often require resource-intensive manual processes that are inefficient and incomplete.
Innovation Solution
A legged robot equipped with sensors providing color and depth images, utilizing a multi-modal open vocabulary object detection model that combines segmentation with depth information to generate hazard maps, allowing for autonomous navigation with updated navigational maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional hazard detection techniques are used, then the robot can identify obstacles, but the detection accuracy is insufficient particularly for obstacles with similar geometries
Solution Approach 1:
The patent segments the image data into multiple regions corresponding to different hazards, allowing the system to analyze each segment individually with specialized models. This segmentation enables precise identification of different hazard types (e.g., cables, steps, holes) by applying appropriate detection strategies to each segment, thereby improving both detection accuracy and adaptability.
Solution Approach 2:
The patent incorporates depth information from depth images as an additional dimension beyond traditional 2D color images. By fusing color and depth data, the system creates multi-dimensional hazard representations that enable better distinction between obstacles with similar geometries but different depth characteristics, significantly improving detection precision and versatility.
2Reliability
If manual hazard identification processes are used, then comprehensive hazard coverage can be achieved, but the process is resource-intensive and inefficient
Solution Approach 1:
The patent implements self-service through autonomous hazard detection where the robot's system automatically identifies, segments, and classifies hazards without manual intervention. The multi-modal machine learning models process sensor data autonomously to generate hazard maps and navigation paths, achieving both comprehensive hazard coverage and high efficiency by eliminating resource-intensive manual processes.
Solution Approach 2:
The patent replaces manual mechanical hazard identification processes with automated computational systems. Instead of human operators manually analyzing environments, the system uses multi-modal machine learning models that process color and depth images computationally to detect and classify hazards, dramatically improving productivity while maintaining reliability through algorithmic consistency.
3Productivity
If simple geometric obstacle detection is used, then the system is computationally efficient, but it cannot provide semantic information about hazards
Solution Approach 1:
The patent merges geometric detection with semantic analysis by integrating color image segmentation and depth information processing. The system combines multiple data modalities (color, depth, segmentation masks) to simultaneously achieve computational efficiency through geometric constraints and rich semantic information through multi-modal fusion, resolving the trade-off between processing speed and information completeness.
Solution Approach 2:
The patent creates composite hazard representations by fusing multiple data types (color image segments, depth information, semantic labels) into a unified hazard map. This composite approach allows the system to maintain computational efficiency from geometric processing while enriching the data with semantic information from multi-modal analysis, achieving both speed and information retention.
Data Source
AI summary
Systems and methods for semantic robot hazard avoidance with multi-modal prompting are provided. In one aspect, a method includes receiving a user input indicative of one or more hazards in an environment of the robot and image data indicative of the one or more hazards in the environment. The method also includes generating one or more segments of the image data. Each of the one or more segments corresponds to at least one of the one or more hazards indicated by the user input. The method further includes identifying a semantic label for each of the one or more segments, generating a hazard map including a location of each of the one or more segments and the corresponding semantic label, and navigating the robot through the environment based at least in part on the hazard map.