Abstract Tuple Classification for Generalizable Robot Instruction Following
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Solution Overview
Problem
Current deep-learning based approaches struggle to execute human instructions effectively, especially when they are underspecified or ambiguous, leading to poor performance on unseen tasks and object classes, as they fail to handle abstract directives and dynamic environments.
Innovation Solution
A system and method for generalizable instruction following using pre-trained language and grounding models, which generates abstract tuples for actions and objects, classifies them into navigation, executable interaction, or complex interaction categories, and decodes complex interactions into executable plans, relying on semantic maps and common-sense candidates from large language models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep-learning based approaches are used to execute human instructions, then the system can handle complex tasks, but it fails to accurately interpret abstract directives and underspecified commands
Solution Approach 1:
The patent segments the instruction execution process into distinct modules: instruction parsing module that breaks down natural language instructions into structured representations, semantic interpretation module that resolves abstract concepts, and execution planning module that generates actionable steps. This segmentation allows each module to specialize in handling specific aspects of instruction following, improving both versatility and accuracy.
Solution Approach 2:
The patent introduces an intermediary semantic representation layer between the input instructions and the execution engine. This intermediary structure acts as a bridge that translates abstract, underspecified natural language into a formal, executable format, enabling accurate interpretation while maintaining the ability to handle diverse task types.
2Ease of operation
If robots attempt to perform tasks with abstract directives, then they can handle human-like instructions, but they encounter inefficient planning and increased execution times
Solution Approach 1:
The patent performs preliminary processing of abstract directives by pre-computing semantic representations and resolving ambiguities before execution planning begins. This preliminary action includes pre-parsing instructions into structured formats and pre-resolving references to objects and locations, which significantly reduces the time required for subsequent execution planning.
Solution Approach 2:
The patent implements a dynamic execution plan that adapts to the specific characteristics of each instruction. The system dynamically adjusts the level of detail in planning based on the complexity of the abstract directive, using heuristic rules to determine the most efficient execution path for different types of tasks, thereby maintaining high productivity while handling diverse human-like instructions.
Data Source
AI summary
Systems and methods for generalizable instruction following are provided. In one embodiment, a method includes generating a plurality of abstract tuples based on a set of instructions. The method includes classifying each abstract tuple of the plurality of abstract tuples as a navigation abstract tuple, an executable interaction abstract tuple, or a complex interaction abstract tuple based on the action. The method includes discarding the abstract tuples classified as navigation abstract tuples. The method includes forming set of executable interactions including the executable interaction abstract tuple. The method includes detecting an object location for the at least one object. The method includes decoding the complex interaction abstract tuples into executable interaction abstract tuples. The method includes adding the decoded executable interaction abstract tuples to the set of executable interactions. The method includes generating an executable plan for an agent based on the set of executable interactions and the object location.