Robot Skill Bundle Distribution for Cross-Workcell Compatibility
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Solution Overview
Problem
Traditional robotics programming requires immense manual effort, is time-consuming, and error-prone, with manually generated programs often being incompatible across different workcells due to varying physical constraints.
Innovation Solution
A skill bundle distribution system that enables the creation, distribution, and execution of reusable skill bundles, which are parameterized to accommodate various robotic systems and include encryption for provenance assurance, allowing for automated reasoning and error identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual programming is used to dictate robotic movements, then precise control of robot actions can be achieved, but the programming process becomes tedious, time-consuming, and error-prone
Solution Approach 1:
The patent segments robotic tasks into reusable skill bundles that can be independently developed, stored in a registry, and distributed to multiple workcells. Each skill bundle represents a modular unit of functionality that can be composed to create complete robot programs, dramatically reducing programming time while maintaining precision through structured task decomposition.
Solution Approach 2:
The patent implements copying by creating reusable skill bundles that can be replicated across multiple workcells and robots. Once a skill is developed and validated in one workcell, it can be copied to the skill registry and distributed to other workcells, eliminating the need to manually reprogram the same tasks for each robot and significantly reducing programming time.
2Reliability
If manual programming is generated for one workcell, then specific task requirements can be met, but the programming cannot be used for other workcells with different physical properties
Solution Approach 1:
The patent achieves universality through the skill registry system, which stores skill bundles that can be used across multiple workcells with different physical properties. Skills are defined in a workcell-agnostic manner and can be adapted to different robots and environments through parameterization, allowing the same skill bundle to serve multiple functions across diverse workcells while maintaining task execution reliability.
Solution Approach 2:
The patent uses parameter changes to adapt skill bundles to different workcells. Skills are defined with configurable parameters that can be adjusted when deploying to different workcells with varying physical properties, such as robot arm lengths, workspace dimensions, and tool configurations. This allows the same skill bundle to be reliably executed across multiple workcells without requiring complete reprogramming.
3Productivity
If skill bundles are distributed across multiple workcells, then reusability and productivity are improved, but the risk of malicious or erroneous skill bundles increases
Solution Approach 1:
The patent implements feedback through a verification system that checks skill bundles before adding them to the registry and before execution. The system provides feedback on whether skills meet required criteria, whether preconditions are satisfied, and whether the skill bundle is valid for the target workcell. This feedback mechanism prevents malicious or erroneous skills from being distributed, maintaining security while enabling widespread reuse for improved productivity.
4Reliability
If comprehensive verification of skill bundles is performed, then safety and reliability are improved, but the complexity of the distribution system increases
Solution Approach 1:
The patent applies preliminary action by performing verification of skill bundles before they are added to the registry and before they are executed by robots. The system checks preconditions, validates skill definitions, and verifies compatibility with target workcells in advance. This preliminary verification ensures safety and reliability without requiring complex runtime checks, as most validation is completed beforehand when the skill bundle is registered.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium that distributes skill bundles that can guide robot execution. One of the methods includes receiving data for a skill bundle from a skill developer. The data can include a definition of one or more preconditions for a robotic system to execute a skill; one or more effects to an operating environment after the robotic system has executed the skill; and a software module implementing the skill. The software module can define a state machine of subtasks. A skill bundle can be generated from the data received from the skill developer. Data identifying the generated skill bundle can be added to a skill registry. The skill bundle can be provided to the execution robot system for installation on the robot execution system.