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The motion of robots and objects in our world is often highly dependent uponcontact. When contact is expected but does not occur or when contact is notexpected but does occur, robot behavior diverges from plan, often disastrously.This paper describes an approach that uses simulation to detect possible suchbehavioral divergences on real robots. This approach, and others like it, couldbe applied to validation of robot behaviors, mechanism design, and even onlineplanning.  The particle trace approach samples robot modeling parameters, sensoryreadings, and state estimates to evaluate a robot's behavior statistically overa range of conditions. We demonstrate that combining even coarse estimates ofstate and modeling parameters with fast multibody simulation can be sufficientto detect divergent robot behavior and characterize robot performance in thereal world. Correspondingly, this approach could be used to assess risk andfind and analyze likely failures, given the extensive data that suchsimulations can generate.  We assess this approach on actuated, high degree-of-freedom robot locomotionexamples, a picking task with a fixed-base manipulator, and an unpoweredpassive dynamic walker. This research works toward understanding howmulti-rigid body simulations can better characterize the behavior of robotswithout significantly compliant elements.

Particle Traces for Detecting Divergent Robot Behavior