Decomposing Compound Attributes for Universal Robot Planning
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Solution Overview
Problem
Current robotic assistance systems are limited to narrow, specialized domains and struggle with planning and execution in broader environments due to the use of compound attributes, which abstract away detailed information and restrict problem-solving capabilities, especially when dealing with unavailability of required devices.
Innovation Solution
The method decomposes compound attributes into elementary attributes, allowing the assistance system to represent actions and effects in a more universal, physical attribute space, enabling the system to plan and execute tasks across different environments and domains by breaking down compound attributes into measurable, low-level attributes that capture underlying physical processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If compound attributes are used to represent environment states, then planning efficiency is improved, but the system loses detailed information and becomes restricted to narrow domains
Solution Approach 1:
The patent segments compound attributes into multiple elementary attributes. Instead of using a single compound attribute to represent an object's state, the system breaks it down into several basic attributes that can be independently manipulated. This allows the planning system to maintain efficiency while preserving detailed information about the environment state.
Solution Approach 2:
The patent introduces a new dimension to the attribute space by adding elementary attributes that expand the representation capacity. This dimensional expansion allows the system to capture more detailed information without sacrificing planning efficiency, as the additional attributes provide finer-grained control over state representation.
2Device complexity
If compound attributes are used for planning, then the planning process is simplified, but the system cannot handle broader environments or missing devices
Solution Approach 1:
The patent creates a universal attribute representation system where elementary attributes can represent various objects and states across different domains. This universal framework allows the same planning algorithms to be applied broadly without domain-specific customization, enhancing adaptability while maintaining planning simplicity.
Solution Approach 2:
The patent changes the parameters of the attribute space by introducing elementary attributes with specific properties that can be combined to represent different states. This parameter transformation allows the system to handle diverse environments and missing devices by flexibly configuring which attributes are present or absent, rather than being constrained by fixed compound attributes.
3Adaptability or versatility
If detailed attribute information is maintained, then the solution space for planning problems increases, but the representation becomes more complex
Solution Approach 1:
The patent segments the attribute representation into modular elementary attributes that can be independently managed. This segmentation increases the solution space by allowing fine-grained state representations while reducing overall complexity through modular organization, making the detailed information more tractable for planning algorithms.
Data Source
AI summary
Method for operating an assistance system based on an attribute space comprises: obtaining task information defining at least one target state to be achieved; obtaining initial state information defining an initial state of an autonomous device or a user, and of at least one object involved in achieving the target state; defining a planning problem based on the task information, the initial state information and attribute information related to the object; selecting an action or a sequence of actions to solve the planning problem; and outputting a control signal to a human-machine interface HMI or the autonomous device for achieving the target state; determining whether the action or sequence of actions achieves the target state, and, if not, decomposing the planning problem based on the attribute information to generate a decomposed planning problem; solving the decomposed planning problem; and outputting the control signal to the HMI or the autonomous device.