Robot Gain Tuning Using Predicted Force for Precision Control
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Solution Overview
Problem
Conventional methods for controlling robots, such as deterministic algorithms and machine learning models trained using simulations, struggle with adapting to real-world variations and require computationally expensive simulations, leading to robot failures or underperformance in high-precision tasks.
Innovation Solution
A computer-implemented method using two trained machine learning models, a force planner and a gain tuner, to generate robot commands that adapt to real-world variations, allowing for precise robot control without the need for highly precise simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If machine learning models are trained using simulations with precise physical interactions, then robot control accuracy in real-world environments is improved, but computational cost and training time increase prohibitively
Solution Approach 1:
The patent uses simplified simulation models that copy only the essential dynamics needed for training, rather than creating expensive precise physical simulations. The simplified models capture sufficient robot-environment interaction characteristics to train effective policies without the computational burden of highly accurate physics engines.
Solution Approach 2:
The patent employs domain randomization techniques that systematically vary simulation parameters (mass, friction, gravity, geometry) during training to improve robustness. This allows the robot to learn invariant policies that transfer well to real-world variations without requiring precisely tuned simulation parameters.
2Manufacturing precision
If machine learning models are trained using simulations with precise physical interactions, then robot control accuracy in real-world environments is improved, but computational resources required increase prohibitively
Solution Approach 1:
The patent creates simplified copies of the physical environment that retain essential dynamic properties while removing computationally expensive details. This enables training on standard hardware without requiring high-performance computing resources.
Solution Approach 2:
The patent uses lightweight simulation models that can be rapidly instantiated and discarded during training, replacing expensive persistent simulation environments. These simplified models consume minimal computational resources while providing sufficient training signal.
3Manufacturing precision
If deterministic algorithms are used to guide the robot through pre-programmed operations, then control precision for high-precision tasks is improved, but adaptability to variations in operating conditions deteriorates
Solution Approach 1:
The patent transitions from static deterministic control to dynamic adaptive control using machine learning policies. These policies continuously adjust control parameters based on real-time sensor feedback and learned relationships, enabling both precision and adaptability to varying conditions.
Solution Approach 2:
The patent implements closed-loop control where the robot receives continuous feedback from sensors and adjusts its actions accordingly. The learned policies incorporate feedback from simulated experiences to adapt to variations in object properties, positioning, and environmental conditions while maintaining precision.
Data Source
AI summary
One embodiment of a method for controlling a robot includes generating, via a first trained machine learning model, a robot motion and a predicted force associated with the robot motion, determining, via a second trained machine learning model, a gain associated with the predicted force, generating one or more robot commands based on the robot motion and the gain, and causing a robot to move based on the one or more robot commands.