let’s high five, heather!
this semester we want roboy to…
how we did it
The aim of this part is to recognize a set of gestures. We decided to use deep learning architectures by using a ROS package which was a pretrained package for shape recognition. In order to train a good model, we need a good dataset. We are currently using a dataset (link) which provides both RGB and depth images.
Recognition part will be connected to gesture execution via ROS. We have implemented the gesture execution to be able to receive the unique ID of different gestures. The received ID will first be sent to the ROS service client. Here, the client will send the unique ID to a service server to receive the joint angles of each finger. Then, this information will be sent back to the client.
get to know the hand team
Team members SS2018
Simon Trendel (Team Lead)
Abhimanyu Sharma (Agile Coach)
Where to go next
a hand for more tasks
- Better Neural Network:
expand dataset with images from different angles
- Real Hand Execution:
use learning algorithm for control
- Stronger Hand:
more powerful hand to perform more tasks
- Grasp Objects