This project is based on the development of a synthetic dataset for grasping basic primitive shapes for cognitive humanoid robots in a simulated environment. * The system will be first trained on a deep learning framework with the annotated dataset with ground truth values for the robo
Grasping with Cognitive Humanoid Robots using Deep learning
This project is based on the development of a synthetic dataset for grasping basic primitive shapes for cognitive humanoid robots in a simulated environment.
* The system will be first trained on a deep learning framework with the annotated dataset with ground truth values for the robotic arm and hand.
* The results generated from the system could be employed on a humanoid robotic hand for various purposes, i.e robotic surgeries, industries, house keeping etc.
development of synthetic dataset for basic primitive shapes in iCub simulated environment.
* generation of various position, orientation and kinematic chain data from the iCub robotic arm for object grasping.
* training a deep learning framework using transfer learning with the dataset of objects to estimate joint angles.
* The comparative analysis with other deep learning frameworks can show robustness of the networks.
Various objects in the iCub simulator will be loaded in front of a table.
* The iCub robotic arm will be used to grab those objects.
* The information related with the arm i.e kinematic chain, position and orientation of the hand will be recorded.
* A dataset with information of the objects will be annotated with kinematic chain and robotic hand.
* A deep learning framework for example AlexNet, will be trained with these information.
The proposed system will provide the ability for humanoid robotic arms for grasping various objects.
* It could be further used in medical sciences i.e robotic surgery, industries, house keeping etc.
humanoid robotic hand grassping for various purposes, i.e robotic surgeries, industries, house keeping etc.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Nvidia Geforce 1080 GPU | Equipment | 1 | 69500 | 69500 |
| Total in (Rs) | 69500 |
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