Robot motion planning has traditionally been used to avoid collisions
when moving a robot arm. Avoiding collisions is important, but many
other desirable criteria are often ignored. For example, motions that
minimize energy will let the robot extend its battery life. Smoother
trajectories may cause less wear on motors and can be more aesthetically
appealing. There may be even more useful criteria, like keeping a glass
of water upright when moving it around.
This summer, Mrinal Kalakrishnan from the Computational Learning and Motor Control Lab
at USC worked on a new motion planner called STOMP, which stands for
"Stochastic Trajectory Optimization for Motion Planning". This planner
can plan paths for high-dimensional robotic systems that are
collision-free, smooth, and can simultaneously satisfy task constraints,
minimize energy consumption, or optimize other arbitrary criteria.
STOMP is derived from gradient-free optimization and path integral
reinforcement learning techniques (Policy Improvement with Path
Integrals, Theodorou et al, 2010).
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