Natural Language Cost Corrections for Adaptive Robot Planning
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Solution Overview
Problem
Existing methods for training robots to adapt to environmental changes or user preferences require extensive human involvement, which is costly, hazardous, and limited in applicability, especially in complex environments.
Innovation Solution
Utilizing natural language inputs to modify a robot's planning objectives through a cost function optimization framework, incorporating symbol grounding and spatio-temporal reasoning to map language corrections to robot actions, allowing for efficient and flexible user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human-in-the-loop policies are used to address robot failures, then reliability is improved, but device complexity and productivity deteriorate due to extensive human involvement
Solution Approach 1:
The patent introduces a language parameterized cost correction module as an intermediary between human users and the robot's motion planner. This module translates natural language corrections into cost function modifications, enabling indirect but efficient human guidance without requiring complex direct control interfaces or extensive human involvement in the control loop.
Solution Approach 2:
The patent replaces traditional mechanical or direct control interfaces with a language-based correction system. Instead of requiring users to manually adjust parameters or directly control robot movements, the system substitutes these mechanical interactions with natural language processing that automatically translates user intent into motion planning corrections.
2Manufacturing precision
If traditional correction methods are used, then manufacturing precision is maintained, but ease of operation deteriorates due to limited accessibility
Solution Approach 1:
The patent substitutes complex technical correction interfaces with natural language input capabilities. Users can provide corrections using everyday language instead of requiring specialized knowledge of robot control systems, motion planning algorithms, or cost function parameters, thereby dramatically improving ease of operation while maintaining precision through the underlying planning framework.
Solution Approach 2:
The language parameterized cost correction module serves as an intermediary that translates accessible natural language inputs into precise technical corrections. This intermediary layer allows users with diverse backgrounds to provide feedback without needing to understand the underlying robotic systems, while still achieving precise task execution through the motion planner.
3Adaptability or versatility
If extensive training data is used, then robot adaptability improves, but loss of time and productivity worsen during training phases
Solution Approach 1:
The patent implements preliminary action by pre-training the robot on diverse environments and tasks to build a robust foundational understanding. This preliminary training phase equips the robot with general adaptability skills, reducing the need for extensive retraining when facing new situations. The language correction capability then allows for rapid fine-tuning without requiring lengthy additional training periods.
Solution Approach 2:
The patent implements continuous feedback mechanisms where the robot receives language-based corrections during operation, allowing it to adapt to environmental changes in real-time. This feedback loop enables the robot to learn and adapt during actual task execution rather than requiring separate, time-consuming training phases, thereby improving both adaptability and productivity simultaneously.
Data Source
AI summary
Approaches presented herein provide for a framework to integrate human provided feedback in natural language to update a robot planning cost or value. The natural language feedback may be modeled as a cost or value associated with completing a task assigned to the robot. This cost or value may then be added to an initial task cost or value to update one or more actions to be performed by the robot. The framework can be applied to both real work and simulated environments where the robot may receive instructions, in natural language, that either provide a goal, modify an existing goal, or provide constraints to actions to achieve an existing goal.