Vision-based Robotic Arm Imitation by Human Gesture
This work addresses the challenge of efficient robot learning for task imitation without requiring 3D reconstruction, though it appears incremental in method.
The paper tackles the problem of enabling a robotic arm to learn complex tasks by imitating human gestures using only monocular camera frames, achieving movement that closely matches human hand motions.
One of the most efficient ways for a learning-based robotic arm to learn to process complex tasks as human, is to directly learn from observing how human complete those tasks, and then imitate. Our idea is based on success of Deep Q-Learning (DQN) algorithm according to reinforcement learning, and then extend to Deep Deterministic Policy Gradient (DDPG) algorithm. We developed a learning-based method, combining modified DDPG and visual imitation network. Our approach acquires frames only from a monocular camera, and no need to either construct a 3D environment or generate actual points. The result we expected during training, was that robot would be able to move as almost the same as how human hands did.