Deep Reinforcement Learning for Wood Structure Assembly
ai-robotics
Traditional timber joint assembly task is hard to automate. Because every piece of wood has different fiber grain, and its shape can change with humidity.
Can robot have human-like intuition through deep learning?
An artificial neural network can learn skills such as playing Go game or controlling robots by interacting with its environment.
Deep Agency is a robotic training and control workflow for adaptive robotic assembly for wood joints
Customized reinforcement learning environment in simulation allows the neural network to explore and improve its skills
Human demonstration on the real world provide data for the agent to learn more efficiently
The trained neural network can be deployed on the real robot through a customized light weight real-time inference control pipeline
