Robot Orchestration for Context-Aware Routine Routing
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Solution Overview
Problem
Current robotics architectures face challenges in creating sophisticated human-robot interactions (HRIs) due to immature machine vision technologies, limited robustness in object manipulation, and a lack of inter-operability between routines, leading to monolithic and compartmentalized systems that struggle to adapt to various scenarios and contexts.
Innovation Solution
The Robot Orchestration Architecture (ROA) enables intuitive and contextual actuation in robots by receiving and processing diverse inputs, employing machine learning to determine appropriate actions, and routing commands to the right routines, supporting modularization, distributed deployment, and plug-and-play integration of machine learning services for advanced context recognition and routine management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a monolithic robotics architecture is used, then system stability is maintained, but adaptability to different scenarios and contexts deteriorates
Solution Approach 1:
The patent segments the robotics architecture into distinct modular components: routines (high-level task definitions), skills (mid-level capabilities), and actions (low-level executable operations). This segmentation allows each component to be independently developed, tested, and reused across different scenarios, thereby improving adaptability without proportionally increasing overall system complexity.
Solution Approach 2:
The patent creates universal interfaces and standardized communication protocols that allow routines, skills, and actions to be reused across multiple different tasks and scenarios. A single skill or action can be invoked by multiple different routines, enabling the system to adapt to various contexts without requiring separate dedicated components for each scenario.
2Productivity
If routine inter-operability is limited, then system reliability is improved, but productivity in handling diverse commands deteriorates
Solution Approach 1:
The patent introduces an intermediary layer (the skill layer) between routines and actions, along with standardized interfaces and communication protocols. This intermediary structure enables routines to interact with multiple skills and actions without direct coupling, improving productivity by allowing diverse command handling while maintaining reliability through standardized interaction patterns and explicit permission checking.
3Adaptability or versatility
If context recognition capabilities are enhanced, then adaptability to scenarios improves, but computational requirements and system complexity increase
Solution Approach 1:
The patent employs preliminary action by pre-defining routines with explicit context parameters and pre-compiled skill-action mappings. When a command is received, the system performs preliminary matching against defined routine contexts rather than analyzing all possible scenarios in real-time, thereby enhancing context recognition capability while controlling processing architecture complexity through pre-computed relationships.
Data Source
AI summary
Implementations directed to providing a computer-implemented system for performing an action with a robot comprising receiving command information indicating a command related to performance of an action with a robot, identifying state information for a plurality of active routines that are actively running for the robot, the state information indicating a state for each of the active routines, determining contextual information for the command based on the accessed state information for the plurality of active routines, selecting one of the active routines as a handling routine to service the command based on the contextual information, determining an output module of the robot to perform the action based on the state of the handling routine and the contextual information, and executing one or more instructions to perform the action with the output module.