Robot Control From Free-Form Language Using Attention-Guided RL
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Solution Overview
Problem
Existing robots struggle to perform tasks in response to free-form natural language inputs, as they typically require explicit user interface mappings and cannot navigate based on complex user instructions.
Innovation Solution
The implementation of deep reinforcement learning to train a model that determines robotic actions at each time step, using current sensor data and free-form natural language input, allowing the robot to process vision data and natural language instructions simultaneously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If explicit user interface mapping is used to control robot tasks, then the robot can reliably perform mapped tasks, but the robot cannot handle free-form natural language inputs
Solution Approach 1:
The model is divided into separate branches: a vision branch that processes sensor data and generates state representations, and a natural language branch that processes language inputs. This segmentation allows each branch to specialize in processing specific types of data independently before integrating their outputs for action determination.
Solution Approach 2:
The patent introduces an attention mechanism as an intermediary between the vision and language branches. This attention mechanism selectively weights and integrates the state representations from vision with the natural language representations, enabling the model to focus on relevant parts of the input data when determining robotic actions.
2Reliability
If the robot processes all sensor data and natural language input equally, then no information is lost, but the robot cannot selectively focus on relevant parts of the input
Solution Approach 1:
The attention mechanism provides feedback by dynamically adjusting the weighting of different input features based on their relevance to the current task context. This feedback loop allows the model to iteratively refine which parts of the sensor data and language input receive more attention, improving the reliability of action determination while preserving critical information.
Solution Approach 2:
The model changes parameters dynamically through the attention mechanism, which adjusts the weights assigned to different parts of the input data based on the current state and task requirements. This parameter adaptation allows the system to focus computational resources on the most relevant information while maintaining the ability to access all original input data when needed.
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.