Robot Control With Target State Correction for Sim-to-Real Transfer
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Solution Overview
Problem
Conventional techniques for training machine learning models to control robots in real-world environments often result in damage to robots and inaccuracies due to simulations not accurately accounting for physical phenomena, leading to ineffective task performance.
Innovation Solution
A method involving a robot control application that receives sensor data, generates actions using a trained machine learning model, computes a target state, and adjusts it based on a threshold to ensure accurate robot movement, even in environments with unmodeled physical phenomena.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-world training is used to train machine learning models for robot control, then the robot can accurately perform tasks in real-world environments, but the robot and objects may be damaged during the data generating process
Solution Approach 1:
The patent creates a virtual copy (simulation environment) of the real-world robot and environment to generate training data. The simulation replicates the robot's physics, sensors, and task environment, allowing the machine learning model to be trained on this virtual copy without risking damage to the physical robot. The trained model is then transferred to control the real robot, resolving the contradiction between needing accurate real-world training data and avoiding physical damage.
2Object-affected harmful factors
If simulations are used to train machine learning models to avoid damage, then the robot and objects are protected, but the simulations do not accurately account for physical phenomena leading to ineffective task performance
Solution Approach 1:
The patent implements a feedback mechanism where the simulation environment is continuously refined based on comparisons between simulated and real-world robot behavior. Sensors on the physical robot collect real-world data about physical phenomena (friction, gravity compensation, etc.), and this feedback is used to update and improve the simulation model's accuracy. This iterative feedback loop ensures the simulation becomes progressively more realistic while still protecting the physical robot during training.
Solution Approach 2:
The patent adjusts physical parameters in the simulation environment (such as friction coefficients, gravitational forces, and joint torques) to better match real-world conditions. By systematically varying and optimizing these parameters based on real-world measurements, the simulation accurately captures physical phenomena without requiring real-world trial and error, thus maintaining both safety and task performance accuracy.
3Reliability
If simulations accurately model all physical phenomena, then the trained model can effectively control the robot in real-world environments, but the simulation complexity and computational requirements increase
Solution Approach 1:
The patent applies local quality by focusing simulation accuracy only on the specific physical phenomena and robot components that are most critical for the task at hand. Rather than attempting to perfectly model all physical aspects of the robot and environment, the simulation concentrates computational resources on accurately representing the local properties that directly impact task performance, such as friction at specific joints or gravity compensation for particular links, while using simplified models for less critical aspects.
Data Source
AI summary
One embodiment of a method for controlling a robot includes receiving sensor data indicating a state of the robot, generating an action based on the sensor data and a trained machine learning model, computing a target state of the robot based on the action and a previous target state of the robot, and causing the robot to move based on the target state of the robot.