Abstract
In recent years, the intelligence production has become an irreversible trend. Being a vital part of high degree of automation, robot arm is also gradually evolving. From only being able to complete a specific task in a fixed manner, it’s now able to decide the behavior itself according to what it “sees”. However, the vision algorithms, either computer vision or deep learning algorithms, usually require a lot of manpower to do pre-processing such as data labeling or camera calibration in order to operate properly, and the algorithm itself often needs human adjustment according to the situations.Inspired by the idea of reducing the manpower required in the algorithm design, we provide an object grasping system that does required any human work for camera calibration. It can learn the target environment directly from raw color image, depth image, and gripper position, outputting the corresponding gripper action. We use convolution neural networks, which have had great success in many fields, to process realistic images from camera, learning how to transform coordinates from 2D images to real 3D space. Also, we take advantage of the powerful environment adaptation capabilities of the reinforcement learning algorithm. Takes the data processed by the convolution neural network as input and learns how to select the action that will promote the success rate of task in the current situation.
We also incorporate the object-centric representation in convolution neural network, and demonstrate that this has a good impact on the system performance with experiments. Changing the method from detect the absolute position of target object to estimate the relative distance of them significantly reduce the burden of learning and finishing the task successfully.
| Date of Award | 2021 |
|---|---|
| Original language | English |
| Supervisor | James Jenn-Jier Lien (Supervisor) |