  Purpose of Review: The field of humanoid robotics, perception plays a
fundamental role in enabling robots to interact seamlessly with humans and
their surroundings, leading to improved safety, efficiency, and user
experience. This scientific study investigates various perception modalities
and techniques employed in humanoid robots, including visual, auditory, and
tactile sensing by exploring recent state-of-the-art approaches for perceiving
and understanding the internal state, the environment, objects, and human
activities.
  Recent Findings: Internal state estimation makes extensive use of Bayesian
filtering methods and optimization techniques based on maximum a-posteriori
formulation by utilizing proprioceptive sensing. In the area of external
environment understanding, with an emphasis on robustness and adaptability to
dynamic, unforeseen environmental changes, the new slew of research discussed
in this study have focused largely on multi-sensor fusion and machine learning
in contrast to the use of hand-crafted, rule-based systems. Human robot
interaction methods have established the importance of contextual information
representation and memory for understanding human intentions.
  Summary: This review summarizes the recent developments and trends in the
field of perception in humanoid robots. Three main areas of application are
identified, namely, internal state estimation, external environment estimation,
and human robot interaction. The applications of diverse sensor modalities in
each of these areas are considered and recent significant works are discussed.
