Perception for Humanoid Robots

TL;DR

This paper reviews recent advances in perception for humanoid robots, focusing on internal state estimation, environment understanding, and human-robot interaction, highlighting sensor fusion and machine learning techniques.

Contribution

It provides a comprehensive overview of state-of-the-art perception methods in humanoid robotics, emphasizing recent trends and technological developments.

Findings

Bayesian filtering and optimization improve internal state estimation.

Multi-sensor fusion enhances external environment understanding.

Contextual information and memory are crucial for human-robot interaction.

Abstract

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.