Virtual Robot Episode Re-simulation for Training Instance Generation
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Solution Overview
Problem
The time and cost associated with training machine learning models for robots through real-world operations can be substantial, as they require a large number of training instances, which can be inefficient and costly.
Innovation Solution
The approach involves generating a plurality of training instances by altering features of recorded user-directed robot control episodes in a simulated 3D virtual environment, including changes to the scene, robot capabilities, and operation, allowing for training without constant human intervention, using a computer-implemented method that simulates a 3D environment, captures user-directed robot control episodes, and trains robot control policies based on these altered instances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If real-world physical robots are used to train machine learning models through repeated task performance, then sufficient training instances can be obtained, but the time and costs associated with training become substantial
Solution Approach 1:
The patent creates virtual copies of physical robots and their operating environments in simulated 3D environments. These digital twins replicate the physical robot's mechanics, sensors, and task performance, allowing training instances to be generated in silico rather than requiring repeated physical robot operations. This copying approach enables parallel generation of numerous training instances without consuming physical robot time.
Solution Approach 2:
The patent performs preliminary actions by capturing and storing robot control episodes (state data, sensor data, control commands) during actual robot operations. These captured episodes are then reused and modified to generate multiple training instances, eliminating the need to repeatedly perform the same physical tasks. The preliminary capture of episode data serves as a foundation for generating extensive training datasets efficiently.
2Quantity of substance
If real-world physical robots are used to train machine learning models, then training data can be obtained, but the costs associated with training become substantial
Solution Approach 1:
By creating virtual replicas of physical robots and environments, the patent eliminates the need for expensive physical robot hardware, specialized facilities, and safety infrastructure during the training phase. The virtual environment can be populated with unlimited variations of objects, terrains, and scenarios at minimal computational cost, making training instance generation economically viable.
Solution Approach 2:
The patent modifies parameters of captured episodes (such as environmental conditions, object positions, robot configurations) to generate diverse training instances from a single recorded episode. This parameter variation approach allows extensive training data generation without requiring proportional increases in physical resources or costs.
3Quantity of substance
If human users manually control simulated robots to perform tasks for imitation learning, then training instances can be generated, but the amount of time users must spend is substantial
Solution Approach 1:
The patent performs preliminary action by having users control the robot only once to capture a complete episode of robot behavior, including state data, sensor readings, and control commands. This single user-directed episode is then algorithmically transformed into multiple training instances through parameter modifications, eliminating the need for users to manually control the robot repeatedly across different scenarios.
Solution Approach 2:
The system automatically varies parameters such as environmental conditions, object properties, and task parameters to generate diverse training instances from a single user-directed episode. This automated parameter transformation replaces manual user intervention with algorithmic variation, dramatically reducing the time users must invest while maintaining training instance diversity.
4Productivity
If a single user-directed robot control episode is captured in simulation, then training data can be obtained, but the number of training instances is insufficient without extensive user time
Solution Approach 1:
The patent segments a single captured episode into multiple reusable components (state transitions, sensor readings, control commands) that can be independently modified and recombined. By segmenting the episode data structure, the system can generate numerous training instances through systematic variation of individual segments, achieving high productivity without requiring proportionally more user time.
Solution Approach 2:
The system applies parameter changes to generate multiple training instances from one episode by varying environmental parameters, object parameters, and task parameters. This parameter-based multiplication transforms a single episode into numerous diverse training scenarios, dramatically increasing training instance quantity while maintaining generation efficiency.
Data Source
AI summary
Implementations are provided for generating a plurality of simulated training instances based on a recorded user-directed robot control episode, and training one or more robot control policies based on such training instances. In various implementations, a three-dimensional environment may be simulated and may include a robot controlled by an external robot controller. A user may operate the robot controller to control the robot in the simulated 3D environment to perform one or more robotic tasks. The user-directed robot control episode, including responses of the external robot controller and the simulated robot to user commands and/or the virtual environment, can be captured. Features of the captured user-directed robot control episode can be altered in order to generate a plurality of training instances. One or more robot control policies can then be trained based on the plurality of training instances.