@proceedings{11,
  author = {Pourya Aliasghari and Moojan Ghafurian and Chrystopher Nehaniv and Kerstin Dautenhahn},
  title = {A Biologically Inspired Program-level Imitation Approach for Robots: Proof-of-Concept},
  abstract = {<p>For social robots to succeed in places such as homes, they must learn new skills from various people and act in a manner desirable to different users. We introduce a novel biologically inspired approach for robot learning through program-level imitation, inspired by the way primates, including humans, understand and perform complex actions. Our approach enables robots to discover the hierarchical structure of tasks by identifying sequential regularities and sub-goals from diverse human demonstrations. To do so, human-provided demonstrations, which can be obtained by a robot through different modalities (such as kinesthetic teaching, behavioural observation, and verbal instruction), are processed by an algorithm that discovers multiple possibilities for arranging observed sub-goals to achieve a final goal. Prior to acting, the available sequences are evaluated based on user-defined criteria, through mental simulation of the task by the robot, to find the optimal sequence of actions. As a proof-of-concept, we implemented our system on an iCub humanoid robot and present here how our method allowed the robot to adapt its action sequences for task execution when starting the task from different states, incorporating user preference for finishing the task as fast as possible. Our envisaged system is meant to accommodate variations in human teaching styles and is expected to help a robot perform tasks with greater flexibility and efficiency. This work contributes by proposing a framework for robots to learn from humans at an abstract level, opening the way to more adaptable and intelligent robotic assistants in everyday tasks.</p>
},
  year = {2024},
  journal = {2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN)},
  volume = {13},
  month = {10/2024},
  publisher = {IEEE},
  address = {Pasadena, CA, USA},
  url = {https://ieeexplore.ieee.org/abstract/document/10731289},
  doi = {10.1109/RO-MAN60168.2024.10731289},
}