Interpolation of Robotic Actions
A novel approach to action combination is proposed by leveraging movement primitives learned through Conditional Neural Motion Planning (CNMP) models. The work introduces a method for generating new actions based on demonstrated ones.
In the research, trajectories are blended by utilizing the task interpolation capabilities of the neural network and the developed mathematical system for parameterization.
The result is the generation of new unseen trajectories adapted to the environment and the different tasks requested.
Testing
The model is tested on the UR10 Robot. Different actions are taught by demonstration, and the network is trained on those skills.
Subsequently, the network is conditioned with different tasks in the same run. The results of the method are shown below.
The results are reproduced in the robot, and the two actions are correctly blended to adapt to the mixed environment.
