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Oren Salzman ( email@example.com) is an
assistant professor in the computer science department
at the Technion–Israel Institute of Technology.
© 2019 ACM 0001-0782/19/10 $15.00.
ning algorithms is looking at the problem from the hardware perspective.
Indeed, recent work suggested minimizing collision detection time by aggressively preprocessing a given scenario for a given robot. This required
designing robot-specific circuitry31 and
it is interesting to see if this approach
can be generalized to non-static environments. Another avenue where
hardware can be exploited is parallelization—motion-planning implementations tend to be highly sequential
and any advances in effective parallelization or amenability to highly parallel paradigms would also help the field
(also see Ichnowski and Alterovitz17
and references within). Finally, recent
advances in cloud-based computation could be highly beneficial and has
raised some initial attention from the
motion-planning community. 22
From the application point of view,
robots are leaving the cages of the industrial manufacturing production
lines and the safety of research labs, and
moving into the unstructured environments of everyday life. From human-in-the-way to human-in-the-loop, modern robotic problems typically involve
robot interactions with and around
humans. Solving such problems requires research in complementary
areas: algorithmic robotics, such as
motion planning and human-robot interaction, such as cognitive modeling,
intention recognition, and activity prediction. Accounting for humans in the
planning domain adds a multitude of
algorithmic constraints—from modeling human behavior to computing consistent, predictable, and safe paths.
However, they also allow for additional
Finally, as robots are being deployed, the robotics community is
collectively gathering experience
and data. Leveraging this experience
and data to improve the efficiency of
planning algorithms is an ongoing
challenge—from incorporating precomputed paths in roadmap-based
algorithms to applying advances in
machine learning to understand when
and how to apply existing tools, or to
develop new tools altogether.
One should not see learning as an
alternative to algorithmic, roadmap-
based planning, but as a complemen-
tary tool—organized search can act
as scaffolding for machine learning
algorithms. While machine learning
exploits correlations between similar
problem instances, search exploits
the structure within a problem. Thus,
the two are quite complementary.
Furthermore, machine learning algo-
rithms are typically data hungry and in
robotics there is often limited access
to huge amounts of real-world data.
For an overview of additional chal-
lenges and opportunities for robot
planning, see Alterovitz et al. 3
To truly impact our world, robot-planning capabilities must be enhanced. To do so, robotic researchers
need to harness tools from other communities and revisit existing, traditional algorithmic tools in order to make
them suitable for the unique, subtle
challenges that arise in this domain.
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