Automatic assembly has broad applications in industries. Traditional assembly tasks utilize predefined trajectories or tuned force control parameters, which make the automatic assembly time-consuming, difficult to generalize, and not robust to uncertainties. In this paper, we propose a learning framework for high precision industrial assembly. The framework combines both the supervised learning and the reinforcement learning. The supervised learning utilizes trajectory optimization to provide the initial guidance to the policy, while the reinforcement learning utilizes actor-critic algorithm to establish the evaluation system even the supervisor is not accurate. The proposed learning framework is more efficient compared with the reinforcement learning and achieves better stability performance than the supervised learning. The effectiveness of the method is verified by both the simulation and experiment.
Equation ) can be solved by optimization once a global dynamics model @math is explicitly modeled. For a contact-rich complex manipulation task, the global dynamics model is extremely difficult to obtain. Therefore, the assembly task either avoids using dynamics @cite_9 or fits the a linear dynamics model @cite_2 @cite_7 @cite_3 .
@cite_9 @cite_3 @cite_7 @cite_2
2963940579 2104733512 2526166478
The high precision assembly of mechanical parts requires precision that exceeds that of robots. Conventional part-mating methods used in the current manufacturing require numerous parameters to be tediously tuned before deployment. We show how a robot can successfully perform a peg-in-hole task with a tight clearance through training a recurrent neural network with reinforcement learning. In addition to reducing manual effort, the proposed method also shows a better fitting performance with a tighter clearance and robustness against positional and angular errors for the peg-in-hole task. The neural network learns to take the optimal action by observing the sensors of a robot to estimate the system state. The advantages of our proposed method are validated experimentally on a 7-axis articulated robot arm. Direct policy search can effectively scale to high-dimensional systems, but complex policies with hundreds of parameters often present a challenge for such methods, requiring numerous samples and often falling into poor local optima. We present a guided policy search algorithm that uses trajectory optimization to direct policy learning and avoid poor local optima. We show how differential dynamic programming can be used to generate suitable guiding samples, and describe a regularized importance sampled policy optimization that incorporates these samples into the policy search. We evaluate the method by learning neural network controllers for planar swimming, hopping, and walking, as well as simulated 3D humanoid running. Programming robotic assembly tasks usually requires delicate force tuning. In contrast, human may accomplish assembly tasks with much less time and fewer trials. It will be a great benefit if robots can learn the human inherent skill of force control and apply it autonomously. Recent works on Learning from Demonstration (LfD) have shown the possibility to teach robots by human demonstration. The basic idea is to collect the force and corrective velocity that human applies during assembly, and then use them to regress a proper gain for the robot admittance controller. However, many of the LfD methods are tested on collaborative robots with compliant joints and relatively large assembly clearance. For industrial robots, the non-backdrivable mechanism and strict tolerance requirement make the assembly tasks more challenging. This paper modifies the original LfD to be suitable for industrial robots. A new demonstration tool is designed to acquire the human demonstration data. The force control gains are learned by Gaussian Mixture Regression (GMR) and the closed-loop stability is analysed. A series of peg-hole-insertion experiments with H7h7 tolerance on a FANUC manipulator validate the performance of the proposed learning method.