Silvia Izquierdo Badiola, Gerard Canal, Carlos Rizzo, Guillem Alenyà
Abstract:
Human-Robot Collaboration (HRC) has become
a major trend in robotics in recent years with the idea of
combining the strengths from both humans and robots. In order
to share the work to be done, many task planning approaches
have been implemented. However, they don’t fully satisfy the
required adaptability in human-robot collaborative tasks, with
most approaches not considering neither the state of the human
partner nor the possibility of adapting the collaborative plan
during execution or even anticipating failures.
In this paper, we present a planning system for human-robot
collaborative plans that takes into account the agents’ states and
deals with unforeseen human behaviour, by replanning in antic-
ipation when the human state changes to prevent action failure.
The human state is defined in terms of capacity, knowledge and
motivation. The system has been implemented in a standardised
environment using the Planning Domain Definition Language
(PDDL) and the modular ROSPlan framework, and we have
validated the approach in multiple simulation settings. Our
results show that using the human model fosters an appropriate
task allocation while allowing failure anticipation, replanning
in time to prevent it.
Paper: Open pdf file.
The following video describes the important concepts of the paper, as presented at ICRA 2022.
This video presents a demonstration for a human-robot collaboration in a bottle recycling scenario. Failures in the collaboration are avoided by directly integrating an agent model in the AI task planning framework definition.