Multi-Task Robot Control With Asymmetric Critic-Guided Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional robot control approaches struggle to generalize across multiple tasks, requiring task-specific training and retraining, which is labor-intensive and inefficient, especially for dexterous manipulation tasks involving high-dimensional visual data.
Innovation Solution
A two-stage training approach using asymmetric critic-guided student models, where expert actor and critic models are trained on privileged data in a simulator, followed by a student actor model trained with behavior cloning loss and distillation loss from expert demonstrations, enabling cross-task learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional robot control approaches use task-specific training, then the robot can achieve high accuracy for a specific task, but the robot requires labor-intensive retraining for each new task
Solution Approach 1:
The patent implements a universal robot control system that can perform multiple different robotic tasks using a single trained model. The system processes various task types (manipulation, navigation, inspection) through a unified architecture that takes sensor data and task identifiers as input, eliminating the need for task-specific retraining while maintaining high execution accuracy across diverse tasks.
Solution Approach 2:
The system performs preliminary training on a diverse set of tasks during the offline phase, preparing a generalizable policy that can adapt to new tasks without retraining. The offline training phase pre-processes multiple task demonstrations and creates a universal policy that anticipates various task requirements, enabling rapid adaptation when new tasks are introduced.
2Measurement precision
If conventional approaches collect task-specific demonstration data, then the training data is accurate for that specific task, but the data collection process is labor-intensive
Solution Approach 1:
The system collects demonstration data that is universally applicable across multiple task types rather than collecting separate task-specific datasets. The data collection process captures diverse manipulations, navigations, and inspections in a unified format, creating a universal demonstration database that serves all task types without requiring separate labor-intensive collection efforts for each task.
Solution Approach 2:
The patent merges multiple task-specific demonstration datasets into a unified universal dataset. Instead of maintaining separate accurate datasets for each task, the system combines demonstrations from various tasks into a single comprehensive training corpus, where diverse examples are integrated and processed through a unified offline training pipeline that extracts generalizable patterns.
3Adaptability or versatility
If the robot control system is designed for high adaptability across tasks, then the robot can perform multiple tasks without retraining, but the system complexity increases
Solution Approach 1:
The patent segments the robot control system into distinct functional modules: an offline training module that processes task demonstrations, an online execution module that implements the trained policy, and a task identifier module that routes tasks appropriately. This segmentation allows the system to achieve high cross-task adaptability while managing complexity through modular architecture, where each component has a specific function and can be independently optimized.
Solution Approach 2:
The system introduces an intermediary offline training pipeline that acts as a mediator between raw task demonstrations and the final control policy. This intermediary layer processes and synthesizes diverse task data into a unified representation, simplifying the transition from complex multi-task requirements to a streamlined online execution system that can handle various tasks without retraining.
Data Source
AI summary
Techniques for training a machine learning model to control a robot include performing, based on a first set of robot data, one or more training operations to generate one or more first trained machine learning models for performing one or more robotic tasks, expert demonstration data, and one or more trained evaluation models; and performing, based on the expert demonstration data, a set of sensor data, and first feedback generated by the one or more trained evaluation models, one or more training operations to generate a second trained machine learning model to control a robot for a plurality of robotic tasks.