Silvia Izquierdo Badiola, Carlos Rizzo, Guillem Alenyà
Abstract: Dealing with the stochastic nature of human behaviour in Human-Robot Collaboration (HRC) remains a well known challenge that needs to be tackled. Automated task planning techniques have been implemented in order to share the workload between the agents, but these still lack the necessary adaptability for real-world applications. In this paper, we extend our previous work presented in [1], where an improved task planning framework integrating an agent model was presented, anticipating and avoiding failures in HRC by reallocating the actions in the plan based on the agents’ states. This work introduces the integration of interaction actions into the planning framework, in order to deal with situations where the issue reflected by a change in an agent state might be better handled with an interaction between the agents than by an action reallocation. Preliminary evaluation shows promising results of how this framework can help to increase the success in HRC plans, as well as the balance in workload distribution between the agents, which constitutes a key element in a collaboration. Paper: Open pdf file.
The following video describes the important concepts of the paper, as presented at IROS Workshop intellect4hri 2022.