Robot Assembly Skill Learning With Actor-Critic Compliance Control
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Solution Overview
Problem
Robotic systems face challenges in performing tight-tolerance assembly operations due to complex misalignments and uncertainties in part poses, leading to inefficient and potentially hazardous manual tuning of force control parameters, with existing reinforcement learning systems being slow and prone to failure.
Innovation Solution
A method using an actor-critic reinforcement learning controller coupled with a compliance controller, where an actor neural network provides target position adjustments based on robot state feedback, and a critic neural network trains the actor to maximize reward, enabling rapid and reliable assembly tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual tuning of force control parameters is used, then the robot can perform assembly tasks with force feedback, but the process is time-consuming and expensive
Solution Approach 1:
The system uses self-service by implementing automated reinforcement learning that allows the robot to autonomously learn and optimize force control parameters without human intervention. The neural networks automatically adjust parameters through trial-and-error learning in simulation, eliminating the need for manual tuning while maintaining reliable assembly task completion.
Solution Approach 2:
The patent replaces the manual mechanical tuning process with an automated computational system. Neural networks and reinforcement learning algorithms substitute for human operators, automatically optimizing force control parameters through digital simulation and optimization, thereby eliminating time-consuming manual trial-and-error tuning.
2Manufacturing precision
If manual tuning is performed on real systems, then parameters can be optimized, but unexpected forceful contact may damage the robot, parts, or fixtures
Solution Approach 1:
The system creates a digital copy or virtual model of the physical assembly environment through simulation. The neural networks are trained in this virtual replica, allowing extensive trial-and-error learning without risking damage to actual robot hardware or physical parts. Once trained in simulation, the optimized parameters are transferred to the real system.
Solution Approach 2:
The patent applies beforehand cushioning by conducting all parameter optimization and trial-and-error learning in a virtual simulation environment before deploying to the physical system. This preparatory training phase cushions against potential damage by absorbing all experimental risks in the digital domain, ensuring safe and optimized parameters are transferred to the real robot.
3Extent of automation
If existing reinforcement learning systems are used, then learning automation is achieved, but training takes a long time and experiences many failed trials
Solution Approach 1:
The system performs preliminary action by pre-training neural networks in a simulated environment before deployment to the real robot. This preliminary training phase allows the system to learn basic skills and optimize parameters rapidly in virtual space, reducing the time and failed trials needed during actual physical operation.
Solution Approach 2:
By creating and using a virtual copy of the physical environment for training, the system enables rapid automated learning without the constraints of physical system limitations. The simulation allows parallel training, faster computation, and unlimited trial-and-error, dramatically improving training speed while maintaining full automation.
Data Source
AI summary
A method and system for robot skill learning applicable to high precision assembly tasks employing a compliance controller. An actor-critic reinforcement learning controller is coupled to the compliance controller, where an actor neural network provides target position adjustment action data to the compliance controller based on robot state data feedback, and a critic neural network is used to train the actor. The critic neural network receives the robot state data feedback and reward data from the robot, along with the action data from the actor, and correlates optimal actions associated with states in order to maximize the reward. The critic then adjusts the parameters of the actor so that the actor produces effective actions in response to the state data, leading to rapid and reliable completion of the assembly task by the compliance controller/robot system. The critic is no longer used after the actor is adequately trained.