Event Location: NSH 3305

Abstract: Motion planning for complex systems such as legged robots and mobile
manipulators has proven to be a difficult task for a variety of reasons:
not only must planning software consider a high-dimensional
configuration space, but it must reason dynamically about how to apply
forces to the real world. Accomplishing such planning in real-time is
harder still. One promising strategy is to take a high-level approach to
planning by reasoning about sequences of discrete behavior primitives.
For many plaforms this has proven far more tractable than global search
from start to goal in the full configuration space of the robot. A
successful example from the field of legged robots is footstep planning,
which reasons over sequences of footsteps.

High-level planning, however, introduces significant tradeoffs. Although
the space of discrete behavior primitives is frequently more efficient
to search, the planner depends on an underlying controller or policy to
faithfully and effectively execute the primitives the planner has
selected. Hence, the planner must have some model of the capabilities of
the controller. Encoding such capability models can be a tedious and
error-prone task: if the capability model is too conservative, the
high-level planner may fail to find solutions for difficult problems; if
the model is too liberal, the robot may fail to execute the selected
behavior. The situation is compounded if the planner must reason about
heterogeneous behaviors—imagine adding hopping and sliding behavior
primitives to an existing footstep planner. It may not be clear how to
translate estimates of the risk and cost of diverse actions into a
common currency.

This thesis aims to develop a general system for quickly and effectively
selecting among heterogeneous behaviors for high-dimensional robotic
navigation and manipulation. The central idea of the system is to store
pre-planned and/or previously executed actions in a behavior library,
which is then analyzed and queried via machine learning techniques. This
data-driven approach can aid high-level planning in a number of ways.
First and foremost, planning is accelerated by re-using the results of
previous computation. In addition, the high-level planner can adapt its
capability model as well as heuristic cost-to-go estimates over time in
order to better reflect the capabilities of the system.

The proposed high-level planning system will be demonstrated and
evaluated on the Boston Dynamics Inc. LittleDog quadruped robot, as well
as on another high-dimensional robotic platform.