  To integrate action recognition methods into autonomous robotic systems, it
is crucial to consider adverse situations involving target occlusions. Such a
scenario, despite its practical relevance, is rarely addressed in existing
self-supervised skeleton-based action recognition methods. To empower robots
with the capacity to address occlusion, we propose a simple and effective
method. We first pre-train using occluded skeleton sequences, then use k-means
clustering (KMeans) on sequence embeddings to group semantically similar
samples. Next, we employ K-nearest-neighbor (KNN) to fill in missing skeleton
data based on the closest sample neighbors. Imputing incomplete skeleton
sequences to create relatively complete sequences as input provides significant
benefits to existing skeleton-based self-supervised models. Meanwhile, building
on the state-of-the-art Partial Spatio-Temporal Learning (PSTL), we introduce
an Occluded Partial Spatio-Temporal Learning (OPSTL) framework. This
enhancement utilizes Adaptive Spatial Masking (ASM) for better use of
high-quality, intact skeletons. The effectiveness of our imputation methods is
verified on the challenging occluded versions of the NTURGB+D 60 and NTURGB+D
120. The source code will be made publicly available at
https://github.com/cyfml/OPSTL.
