  Legged robots can traverse a wide variety of terrains, some of which may be
challenging for wheeled robots, such as stairs or highly uneven surfaces.
However, quadruped robots face stability challenges on slippery surfaces. This
can be resolved by adjusting the robot's locomotion by switching to more
conservative and stable locomotion modes, such as crawl mode (where three feet
are in contact with the ground always) or amble mode (where one foot touches
down at a time) to prevent potential falls. To tackle these challenges, we
propose an approach to learn a model from past robot experience for predictive
detection of potential failures. Accordingly, we trigger gait switching merely
based on proprioceptive sensory information. To learn this predictive model, we
propose a semi-supervised process for detecting and annotating ground truth
slip events in two stages: We first detect abnormal occurrences in the time
series sequences of the gait data using an unsupervised anomaly detector, and
then, the anomalies are verified with expert human knowledge in a replay
simulation to assert the event of a slip. These annotated slip events are then
used as ground truth examples to train an ensemble decision learner for
predicting slip probabilities across terrains for traversability. We analyze
our model on data recorded by a legged robot on multiple sites with slippery
terrain. We demonstrate that a potential slip event can be predicted up to 720
ms ahead of a potential fall with an average precision greater than 0.95 and an
average F-score of 0.82. Finally, we validate our approach in real-time by
deploying it on a legged robot and switching its gait mode based on slip event
detection.
