  One of the fundamental challenges in realizing the potential of legged robots
is generating plans to traverse challenging terrains. Control actions must be
carefully selected so the robot will not crash or slip. The high dimensionality
of the joint space makes directly planning low-level actions from onboard
perception difficult, and control stacks that do not consider the low-level
mechanisms of the robot in planning are ill-suited to handle fine-grained
obstacles. One method for dealing with this is selecting footstep locations
based on terrain characteristics. However, incorporating robot dynamics into
footstep planning requires significant computation, much more than in the
quasi-static case. In this work, we present an LSTM-based planning framework
that learns probability distributions over likely footstep locations using both
terrain lookahead and the robot's dynamics, and leverages the LSTM's sequential
nature to find footsteps in linear time. Our framework can also be used as a
module to speed up sampling-based planners. We validate our approach on a
simulated one-legged hopper over a variety of uneven terrains.
