A framework for trust-related knowledge transfer in human-robot interaction

Trustworthy human-robot interaction (HRI) during activities of daily living (ADL) presents an interesting and challenging domain for assistive robots, particularly since methods for estimating the trust level of a human participant towards the assistive robot are still in their infancy. Trust is a multifaced concept which is affected by the interactions between the robot and the human, and depends, among other factors, on the history of the robot's functionality, the task and the environmental state. In this paper, we are concerned with the challenge of trust transfer, i.e. whether experiences from interactions on a previous collaborative task can be taken into consideration in the trust level inference for a new collaborative task. This has the potential of avoiding re-computing trust levels from scratch for every new situation. The key challenge here is to automatically evaluate the similarity between the original and the novel situation, then adapt the robot's behaviour to the novel situation using previous experience with various objects and tasks. To achieve this, we measure the semantic similarity between concepts in knowledge graphs (KGs) and adapt the robot's actions towards a specific user based on personalised interaction histories. These actions are grounded and then verified before execution using a geometric motion planner to generate feasible trajectories in novel situations. This framework has been experimentally tested in human-robot handover tasks in different kitchen scene contexts. We conclude that trust-related knowledge positively influences and improves collaboration in both performance and time aspects.