Goal-Conditioned Robot Control Using Shared Latent Task Representations
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Solution Overview
Problem
Existing robotic systems struggle to perform tasks in response to free-form natural language inputs, requiring explicit user interface mappings and lacking the ability to navigate to goal locations based on user requests.
Innovation Solution
Training a goal-conditioned policy network using multiple datasets, including goal images, natural language text, and task IDs, to generate a shared latent goal space representation, allowing robots to understand and execute tasks described in various forms of input, with a focus on teleoperated play data to generate diverse and unconstrained task descriptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If explicit user interface mappings are used to control robot tasks, then the robot can perform specific programmed tasks, but the robot cannot understand free-form natural language inputs
Solution Approach 1:
The patent introduces an intermediary natural language processing system that translates free-form user inputs into robot-executable commands. This intermediary layer enables the robot to understand diverse natural language expressions without requiring explicit pre-programmed mappings for each possible input, thereby increasing adaptability while managing complexity through a unified translation framework.
Solution Approach 2:
The patent replaces the mechanical mapping system (explicit if-then mappings between UI inputs and robot actions) with an intelligent processing system using natural language understanding. This substitution allows the robot to interpret the meaning and intent of free-form inputs rather than relying on rigid predefined mappings, significantly enhancing versatility.
2Adaptability or versatility
If multiple datasets with different task descriptions are used to train the policy network, then the robot can perform tasks in various formats, but the training complexity and data processing requirements increase
Solution Approach 1:
The patent implements a universal policy network architecture that can process multiple types of task descriptions (goal images, natural language text, task IDs) through a unified training framework. This multi-functional system learns to map different input formats to a common latent goal space, enabling the robot to perform tasks specified in various formats without requiring separate specialized systems for each input type.
Solution Approach 2:
The patent transforms different task description formats into a standardized latent representation space through learned encoders. By changing the parameter representation of diverse inputs (images, text, IDs) into a common latent format, the system simplifies the training process and enables unified policy learning across multiple data types while maintaining adaptability to various input formats.
3Adaptability or versatility
If teleoperated play data is used to generate training datasets, then diverse and unconstrained task descriptions can be collected, but the data processing and segmentation requirements increase
Solution Approach 1:
The patent performs preliminary processing of teleoperated play data during the data collection phase, organizing raw data into structured segments that can be easily reused for training. By pre-segmenting and labeling data as it is collected, rather than processing it afterward, the system reduces the time required for subsequent data preparation while maintaining high task diversity from unconstrained teleoperated sessions.
Data Source
AI summary
Techniques are disclosed that enable training a goal-conditioned policy based on multiple data sets, where each of the data sets describes a robot task in a different way. For example, the multiple data sets can include: a goal image data set, where the task is captured in the goal image; a natural language instruction data set, where the task is described in the natural language instruction; a task ID data set, where the task is described by the task ID, etc. In various implementations, each of the multiple data sets has a corresponding encoder, where the encoders are trained to generate a shared latent space representation of the corresponding task description. Additional or alternative techniques are disclosed that enable control of a robot using a goal-conditioned policy network. For example, the robot can be controlled, using the goal-conditioned policy network, based on free-form natural language input describing robot task(s).