Robot Skill Learning With Offline Pre-Training for Precision Assembly
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing robotic assembly systems face challenges in performing tight-tolerance assembly tasks due to positioning uncertainties and require manual tuning, which is time-consuming, expensive, and hazardous, while existing learning systems lack robustness and efficiency.
Innovation Solution
A method involving a reinforcement learning controller pre-trained offline using human demonstration data, followed by online self-learning with human override, to improve robotic assembly skills using compliance controllers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual tuning of force control parameters is performed on real robotic systems, then the assembly task can be completed with proper force control, but the process becomes time-consuming and expensive due to trial and error
Solution Approach 1:
The patent creates a digital twin (simulation environment) that replicates the physical robotic system's dynamics, sensors, and assembly task. This virtual copy allows for rapid iteration and testing of force control parameters without affecting the real system, eliminating time-consuming trial and error on actual hardware while maintaining accurate representation of the physical system's behavior
Solution Approach 2:
The patent performs force control parameter tuning in advance within the simulation environment before deploying to the real robotic system. By pre-adjusting parameters in the virtual model and validating performance beforehand, the system eliminates the need for lengthy on-site tuning, significantly reducing setup time and enabling faster deployment to multiple robotic systems
2Reliability
If parameter tuning is performed on real physical test systems, then the force control can be optimized, but the process becomes hazardous since robots are not compliant and unexpected forceful contact may damage the robot, parts, or fixtures
Solution Approach 1:
The patent uses a faithful digital replica of the physical system that includes accurate modeling of robot dynamics, compliance controller behavior, and assembly task requirements. This virtual copy enables safe exploration of extreme force control parameters and failure modes without any risk to physical equipment, parts, or fixtures, while still providing meaningful insights for optimizing real system performance
Solution Approach 2:
The patent implements a virtual safety buffer by conducting all hazardous parameter tuning and validation in the simulation environment first. The digital twin absorbs all potential damage risks during the tuning phase, and only after successful validation in the virtual world are the optimized parameters transferred to the real system, effectively cushioning the physical system from exposure to harmful test conditions
3Extent of automation
If existing imitation learning systems are used for robotic assembly, then learning can be performed without manual tuning, but the systems lack robustness and failure data overwhelms the demonstration data
Solution Approach 1:
The patent introduces a reward shaping mechanism that acts as an intermediary between the demonstration data and the learning algorithm. By designing custom reward functions that explicitly penalize failure modes and emphasize successful assembly behaviors, the system guides the learning process to prioritize successful demonstrations, preventing failure data from overwhelming the training signal and improving learning robustness
Solution Approach 2:
The patent transforms the learning problem by changing the reward function parameters to reflect domain-specific success criteria for assembly tasks. By carefully tuning reward weights and thresholds based on expert knowledge, the system amplifies the signal from successful demonstrations while suppressing the influence of failures, enabling robust learning even with limited high-quality demonstration data
4Adaptability or versatility
If existing reinforcement learning systems are used for robotic assembly, then the system can learn complex tasks, but training takes a long time and experiences many failed trials with risk of part damage and hazardous behavior
Solution Approach 1:
The patent performs the bulk of reinforcement learning training in the simulation environment before deployment to the real robotic system. By pre-training the policy in the virtual world where time can be accelerated and failures are harmless, the system achieves competent performance without lengthy real-world training, significantly reducing the time and resource requirements for learning complex assembly tasks
Solution Approach 2:
The patent uses a high-fidelity simulation environment that accurately replicates the physical system's dynamics, sensors, and assembly task. This digital twin allows the robot to undergo extensive reinforcement learning training in virtual reality, accumulating experience and learning complex skills without consuming real time or risking physical damage, then transferring the learned policy to the actual robot for execution
Data Source
AI summary
A method and system for robot skill learning for high precision assembly tasks employing a compliance controller. A reinforcement learning (RL) controller is pre-trained in an offline mode using human demonstration data, where several repetitions of the demonstration are performed while collecting state and action data for each repetition. The demonstration data is used to pre-train a neural network in the RL controller, with no interaction of the RL controller with the compliance controller/robot system. Following pre-training, the RL controller is moved to online production where it is coupled to the compliance controller/robot system in a self-learning mode. During self-learning, the neural network-based RL controller uses action, state and reward data to continue learning correlations between states and effective actions. Co-training is provided as needed during self-learning, where a human operator overrides the RL controller actions to ensure successful assembly operations, which improves the learned performance of the RL controller.