Robot Skill Learning With Offline Pretraining for Precision Assembly
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Solution Overview
Problem
Industrial robots 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 reinforcement learning controller is pre-trained offline using human demonstration data and then coupled to a compliance controller/robot system for self-learning, with co-training by a human operator to ensure successful assembly operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual tuning of force control parameters is performed on real robotic systems, then the robot can be configured for assembly tasks, but the process is time-consuming and hazardous
Solution Approach 1:
The patent creates a virtual copy of the robotic system through simulation environment that replicates the physical system's dynamics and assembly task requirements. This virtual model allows all tuning operations to be performed safely without risking damage to actual robot or parts, while maintaining fidelity to the real system's behavior for accurate parameter transfer
Solution Approach 2:
The system performs force control parameter tuning in advance within the simulation environment before deploying to the real robotic system. By completing the time-consuming trial-and-error tuning process beforehand in a safe virtual setting, the actual robot deployment requires minimal or no tuning, significantly reducing on-site setup time and eliminating safety hazards
2Extent of automation
If reinforcement learning systems are used for robot skill learning, then automation is improved, but training time is long and failure rate is high
Solution Approach 1:
The system pre-generates a comprehensive dataset of successful assembly demonstrations by having human operators perform tasks in the simulation environment. This pre-collected data serves as high-quality training material that captures correct assembly behaviors, allowing the reinforcement learning algorithm to learn from proven successful examples rather than discovering through numerous failed trials, thus dramatically reducing training time while maintaining full automation
Solution Approach 2:
The system implements a feedback mechanism where the reinforcement learning controller receives reward signals based on its performance in the simulation environment. Successful assembly actions generate positive rewards that guide the learning process, enabling the system to efficiently identify effective strategies without extensive trial-and-error, thereby reducing training time while maintaining automation
3Loss of time
If imitation learning systems are used for force control, then setup time is reduced, but robustness is poor and failure data overwhelms demonstration data
Solution Approach 1:
The system introduces a simulation environment as an intermediary layer between human demonstration and the learning controller. This intermediate virtual environment allows demonstration data to be generated under controlled conditions with guaranteed successful outcomes, filtering out failure data before it reaches the learning system. The simulation acts as a mediator that translates human skills into clean training data, maintaining fast setup while improving robustness
Solution Approach 2:
The system creates multiple virtual copies of successful assembly demonstrations through simulation, generating abundant high-quality training data that reflects successful behaviors. By copying proven successful patterns from human operators in the virtual environment, the system ensures that demonstration data dominates the training set, preventing failure data from overwhelming the learning process while maintaining rapid setup through automated data generation
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.