ROAug 4, 2016

Particle Traces for Detecting Divergent Robot Behavior

arXiv:1608.01606v1
Originality Incremental advance
AI Analysis

This work addresses the challenge of validating robot behaviors and assessing failure risks in robotics, though it appears incremental as it builds on existing simulation-based methods.

The paper tackles the problem of detecting divergent robot behavior caused by unexpected contact events by using a particle trace approach that samples modeling parameters and sensory readings to evaluate robot behavior statistically across various conditions. The result demonstrates that combining coarse state estimates with fast multibody simulation can effectively detect divergent behavior and characterize performance in real-world tasks like locomotion, picking, and passive walking.

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.

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