Robot Control From Free-Form Language With Visual Attention
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Solution Overview
Problem
Current robotic systems are unable to perform tasks in response to free-form natural language inputs, limiting their ability to navigate or execute instructions based on user commands that are not explicitly mapped.
Innovation Solution
A deep reinforcement learning model is trained to determine robotic actions at each time step using current sensor data and free-form natural language input, processing vision data and natural language inputs through separate branches to generate action predictions, enabling the robot to follow complex instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If explicit user interface input mapping is used to control robot tasks, then the robot can reliably perform mapped tasks, but the robot cannot perform tasks in response to free-form natural language inputs
Solution Approach 1:
The patent introduces an attention mechanism as an intermediary between the natural language processing branch and the vision/state representation branches. This attention mechanism selectively weights different parts of the natural language input based on the current visual context, enabling the robot to reliably interpret free-form natural language commands by focusing on relevant instructions while maintaining task execution reliability
Solution Approach 2:
The patent segments the control system into distinct branches: a natural language processing branch that handles free-form input, a vision processing branch that processes sensor data, and an attention mechanism that integrates them. This segmentation allows each branch to specialize in its function while the attention mechanism coordinates their interaction, enabling both versatility in input acceptance and reliability in task execution
2Productivity
If the robot processes all natural language input uniformly, then simple commands can be followed, but complex instructions with multiple steps and landmarks cannot be accurately executed
Solution Approach 1:
The patent implements a dynamic attention mechanism that adaptively adjusts the weighting of different natural language input elements based on the current visual context and robot state. This dynamic processing allows the system to handle complex multi-step instructions efficiently by focusing computational resources on the most relevant parts of the command at each time step, improving productivity without requiring excessive processing complexity
Data Source
AI summary
Implementations relate to using deep reinforcement learning to train a model that can be utilized, at each of a plurality of time steps, to determine a corresponding robotic action for completing a robotic task. Implementations additionally or alternatively relate to utilization of such a model in controlling a robot. The robotic action determined at a given time step utilizing such a model can be based on: current sensor data associated with the robot for the given time step, and free-form natural language input provided by a user. The free-form natural language input can direct the robot to accomplish a particular task, optionally with reference to one or more intermediary steps for accomplishing the particular task. For example, the free-form natural language input can direct the robot to navigate to a particular landmark, with reference to one or more intermediary landmarks to be encountered in navigating to the particular landmark.