6 research outputs found
Adapting to Human Preferences to Lead or Follow in Human-Robot Collaboration: A System Evaluation
With the introduction of collaborative robots, humans and robots can now work
together in close proximity and share the same workspace. However, this
collaboration presents various challenges that need to be addressed to ensure
seamless cooperation between the agents. This paper focuses on task planning
for human-robot collaboration, taking into account the human's performance and
their preference for following or leading. Unlike conventional task allocation
methods, the proposed system allows both the robot and human to select and
assign tasks to each other. Our previous studies evaluated the proposed
framework in a computer simulation environment. This paper extends the research
by implementing the algorithm in a real scenario where a human collaborates
with a Fetch mobile manipulator robot. We briefly describe the experimental
setup, procedure and implementation of the planned user study. As a first step,
in this paper, we report on a system evaluation study where the experimenter
enacted different possible behaviours in terms of leader/follower preferences
that can occur in a user study. Results show that the robot can adapt and
respond appropriately to different human agent behaviours, enacted by the
experimenter. A future user study will evaluate the system with human
participants