2 research outputs found
Collective Intelligence for Object Manipulation with Mobile Robots
While natural systems often present collective intelligence that allows them
to self-organize and adapt to changes, the equivalent is missing in most
artificial systems. We explore the possibility of such a system in the context
of cooperative object manipulation using mobile robots. Although conventional
works demonstrate potential solutions for the problem in restricted settings,
they have computational and learning difficulties. More importantly, these
systems do not possess the ability to adapt when facing environmental changes.
In this work, we show that by distilling a planner derived from a
gradient-based soft-body physics simulator into an attention-based neural
network, our multi-robot manipulation system can achieve better performance
than baselines. In addition, our system also generalizes to unseen
configurations during training and is able to adapt toward task completions
when external turbulence and environmental changes are applied
World Robot Challenge 2020 -- Partner Robot: A Data-Driven Approach for Room Tidying with Mobile Manipulator
Tidying up a household environment using a mobile manipulator poses various
challenges in robotics, such as adaptation to large real-world environmental
variations, and safe and robust deployment in the presence of humans.The
Partner Robot Challenge in World Robot Challenge (WRC) 2020, a global
competition held in September 2021, benchmarked tidying tasks in the real home
environments, and importantly, tested for full system performances.For this
challenge, we developed an entire household service robot system, which
leverages a data-driven approach to adapt to numerous edge cases that occur
during the execution, instead of classical manual pre-programmed solutions. In
this paper, we describe the core ingredients of the proposed robot system,
including visual recognition, object manipulation, and motion planning. Our
robot system won the second prize, verifying the effectiveness and potential of
data-driven robot systems for mobile manipulation in home environments