Robot Control With Multi-Modal Language Planning
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Solution Overview
Problem
Existing robotics planning systems are limited by requiring predefined command words or phrases for controlling robots, leading to inefficient use of computational resources and reduced flexibility in task execution.
Innovation Solution
A multi-modal robotics planning system that integrates a language model neural network with multi-modal prompts to generate high-quality plans from textual and image data, allowing robots to be controlled using natural language inputs and reducing the need for multiple control iterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional robotics planning systems use predefined command words or phrases for controlling robots, then the system structure is simple and easy to implement, but the flexibility in task execution is reduced and computational resources are used inefficiently
Solution Approach 1:
The patent replaces traditional mechanical control systems with predefined command words and phrases with a multi-modal language model neural network that processes natural language inputs. This substitution enables the robot to understand and execute a broader range of tasks using human-like language, significantly improving flexibility while the modular architecture of the neural network keeps implementation complexity manageable
Solution Approach 2:
The patent implements a universal control system using a multi-modal language model that can handle multiple types of inputs (text, images, audio) and generate diverse outputs for various robot actions. This single system replaces multiple specialized control modules, achieving versatility across different task types while maintaining a unified, manageable system structure
2Productivity
If conventional systems control the robot to perform actions that are individually selected one after another over multiple control iterations, then the control precision can be maintained, but the use of computational resources becomes inefficient
Solution Approach 1:
The patent employs a planning module that generates a complete sequence of actions in advance before execution begins. This preliminary planning allows the robot to understand the entire task trajectory upfront, reducing the need for multiple iterative control adjustments and improving computational efficiency by minimizing repeated processing cycles
Solution Approach 2:
The patent implements continuous planning and execution where the language model generates actionable plans that flow seamlessly from one step to the next without interruption. This continuous action approach eliminates gaps between control iterations, maintaining productivity while reducing the overall time required for task completion
3Productivity
If a robotics planning system generates plans that include a sequence of multiple actions, then computational resources are used more efficiently with fewer control iterations, but the complexity of plan generation and processing increases
Solution Approach 1:
The patent segments the complex plan generation process into distinct functional modules: a perception module for processing multi-modal inputs, a language model for generating natural language plans, and an execution module for translating plans into robot actions. This segmentation reduces the complexity of any single module while maintaining the overall efficiency of generating multi-action plans in fewer control iterations
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling an agent interacting with an environment. In one aspect, a method comprises: receiving one or more observations of an environment; receiving an input text sequence that describes a task to be performed by a robot in the environment; generating an encoded representation of the input text sequence in an embedding space; generating a corresponding encoded representation of each of the one or more observations in the embedding space; generating a sequence of input tokens that comprises the encoded representation of the input text sequence and the corresponding encoded representation of each observation; processing the sequence of input tokens using a language model neural network to generate an output text sequence that comprises high-level natural language instructions; and determining, from the high-level natural language instructions, one or more actions to be performed by the robot.