Goal-Conditioned Robot Control for Free-Form Language Tasks
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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 formats, with a focus on teleoperated play data for efficient data collection and multicontext imitation learning.
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 reliably, but the robot cannot understand or perform tasks from free-form natural language inputs
Solution Approach 1:
The patent introduces an intermediary natural language processing system that translates free-form user inputs into structured task representations. This intermediary layer includes language models and parsing mechanisms that bridge the gap between natural language and robot control commands, allowing the robot to understand diverse natural language inputs without requiring explicit pre-programmed mappings for every possible command.
Solution Approach 2:
The patent implements a universal task representation framework that can handle multiple types of inputs (natural language, structured commands, goal images) through a single integrated system. This multi-functional approach allows the robot to process various input formats and convert them into a common internal representation, enabling flexible task execution across different communication modalities without requiring separate specialized systems.
2Adaptability or versatility
If multiple datasets with different task descriptions are used to train the policy network, then the robot can perform diverse tasks in various formats, but the training data collection becomes more complex and resource-intensive
Solution Approach 1:
The patent employs data augmentation techniques that create synthetic training examples by copying and transforming existing data. Natural language descriptions are generated through language models, goal images are synthesized using graphics engines, and task variations are created by copying base tasks with modified parameters. This copying approach expands the training dataset diversity without requiring proportional increases in human annotation effort.
Solution Approach 2:
The patent performs preliminary data processing and preprocessing steps before main training, including pre-training language models on large corpora, pre-generating goal images from task descriptions, and pre-structuring raw data into standardized formats. These preliminary actions prepare the data in advance, reducing the complexity of the main training process and enabling efficient multi-format task learning.
3Reliability
If human-provided natural language data is collected extensively, then the robot achieves better natural language understanding, but the data collection process becomes costly and time-consuming
Solution Approach 1:
The patent implements self-service mechanisms where the system generates its own training data through automated processes. Language models generate synthetic natural language instructions, robots perform self-supervised learning from their own operation logs and sensor data, and the system automatically annotates its experiences. This self-service approach reduces dependency on extensive human annotation while maintaining reliable natural language understanding through continuous self-learning.
Solution Approach 2:
The patent incorporates feedback loops where the robot's performance on natural language tasks is continuously monitored and used to improve the training data quality. unsuccessful attempts generate corrective feedback that refines the language-task mappings, and performance metrics guide targeted data collection efforts. This feedback mechanism ensures reliable understanding while optimizing the time investment in data collection by focusing resources on the most impactful improvements.
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).