Robot Task Planning for Interdependent Natural Language Instructions
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Solution Overview
Problem
Existing robotic task planning systems struggle to execute complex natural language instructions that include multiple inter-dependent tasks, as they fail to accurately understand task dependencies and linguistic variations, leading to limited generalization and high annotation requirements.
Innovation Solution
A processor-implemented method using a Conditional Random Field (CRF) model to predict task dependency labels and generate a Conditional Planning Tree (CPT) from a set of tokens and task type labels, allowing for the modification of original task sequences to resolve task dependencies and execute intended tasks effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based parsers are used to understand and generate task plans from complex instructions, then the system can handle structured language specifications, but it cannot handle linguistic variations
Solution Approach 1:
The patent replaces rule-based parsers (mechanical system) with neural network models that use soft attention mechanisms to process natural language instructions. This substitution enables the system to handle linguistic variations while maintaining task plan understanding accuracy through learned patterns rather than rigid rules.
Solution Approach 2:
The system changes the parameter of language processing from fixed rule-based structures to dynamic neural representations with attention weights. This allows the model to adapt to different linguistic expressions by adjusting attention parameters rather than relying on predetermined parsing rules.
2Productivity
If multiple independent or serialized tasks are given in an instruction to the robot, then the robot can handle multiple tasks, but it fails to understand inter-dependent tasks with conditional statements
Solution Approach 1:
The patent introduces an intermediary representation layer between the natural language input and task execution. This intermediary uses attention mechanisms to model task dependencies and conditional relationships, enabling the robot to understand complex inter-dependent tasks while maintaining multiple task handling capability.
Solution Approach 2:
The system adds a temporal and dependency dimension to task representation by using recurrent neural networks and attention mechanisms. This transforms the flat sequence of tasks into a structured representation that captures conditional relationships and execution order, improving task dependency understanding.
3Measurement precision
If significant annotation effort is required to introduce a new task, then the system can be trained accurately, but it becomes complex and time-consuming to adapt to new tasks
Solution Approach 1:
The system employs self-attention mechanisms that allow the model to automatically learn task representations and dependencies from the input data without requiring extensive manual annotation. The attention weights are learned automatically during training, reducing the need for hand-crafted annotations while maintaining training accuracy.
Data Source
AI summary
This disclosure provides systems and methods for robotic task planning when a complex task instruction is provided in natural language. Conventionally robotic task planning relies on a single task or multiple independent or serialized tasks in the task instruction. Alternatively, constraints on space of linguistic variations, ambiguity and complexity of the language may be imposed. In the present disclosure, firstly dependencies between multiple tasks are identified. The tasks are then ordered such that a dependent task is always scheduled for planning after a task it is dependent upon. Moreover, repeated tasks are masked. Thus, resolving task dependencies and ordering dependencies, a complex instruction with multiple interdependent tasks in natural language facilitates generation of a viable task execution plan. Systems and methods of the present disclosure finds application in human-robot interactions.