Learning Robotic Assembly from CAD

In this work, motivated by recent manufacturing trends, we investigate autonomous robotic assembly. Industrial assembly tasks require contact-rich manipulation skills, which are challenging to acquire using classical control and motion planning approaches. Consequently, robot controllers for assembly domains are presently engineered to solve a particular task, and cannot easily handle variations in the product or environment. Reinforcement learning (RL) is a promising approach for autonomously acquiring robot skills that involve contact-rich dynamics. However, RL relies on random exploration for learning a control policy, which requires many robot executions, and often gets trapped in locally suboptimal solutions. Instead, we posit that prior knowledge, when available, can improve RL performance. We exploit the fact that in modern assembly domains, geometric information about the task is readily available via the CAD design files. We propose to leverage this prior knowledge by guiding RL along a geometric motion plan, calculated using the CAD data. We show that our approach effectively improves over traditional control approaches for tracking the motion plan, and can solve assembly tasks that require high precision, even without accurate state estimation. In addition, we propose a neural network architecture that can learn to track the motion plan, thereby generalizing the assembly controller to changes in the object positions.

Authors

• Garrett Thomas
• Melissa Chien
• Aviv Tamar
• Juan Aparicio Ojea
• Pieter Abbeel

Keywords

• Task analysis
• Planning
• Robots
• Trajectory
• Tracking
• Robotic assembly
• Dynamics
• Neural Network
• Path Planning
• Geometric Information
• Assembly Domain
• Accurate State Estimation
• Training Set
• Supervised Learning
• Cost Function
• Tracking Control
• Trajectory Optimization
• Policy Learning
• Learning Control
• Reference Trajectory
• Robot Motion
• Dynamic Contact
• Object Pose
• Task Space
• Goal Position
• Training Configurations
• Policy Search
• Iterative Learning Control
• Proportional-derivative Control
• CAD File
• Initialization Procedure
• Trajectory Tracking Control
• Binocular
• Attention Mechanism
• Manipulation Tasks
• Friction