Towards Robot Learning from Spoken Language
Krishna Kodur, Manizheh Zand, Maria Kyrarini · 2023
The paper proposes a robot learning framework that empowers a robot to automatically generate a sequence of actions from unstructured spoken language. The robot learning framework was able to distinguish between instructions and unrelated conversations. Data were collected from 25 participants, who were asked to instruct the robot to perform a collaborative cooking task while being interrupted and distracted. The system was able to identify the sequence of instructed actions for a cooking task with an accuracy of of 92.85 ± 3.87%.