  The motion of robots and objects in our world is often highly dependent upon
contact. When contact is expected but does not occur or when contact is not
expected but does occur, robot behavior diverges from plan, often disastrously.
This paper describes an approach that uses simulation to detect possible such
behavioral divergences on real robots. This approach, and others like it, could
be applied to validation of robot behaviors, mechanism design, and even online
planning.
  The particle trace approach samples robot modeling parameters, sensory
readings, and state estimates to evaluate a robot's behavior statistically over
a range of conditions. We demonstrate that combining even coarse estimates of
state and modeling parameters with fast multibody simulation can be sufficient
to detect divergent robot behavior and characterize robot performance in the
real world. Correspondingly, this approach could be used to assess risk and
find and analyze likely failures, given the extensive data that such
simulations can generate.
  We assess this approach on actuated, high degree-of-freedom robot locomotion
examples, a picking task with a fixed-base manipulator, and an unpowered
passive dynamic walker. This research works toward understanding how
multi-rigid body simulations can better characterize the behavior of robots
without significantly compliant elements.
