HRI aims to study and enhance the relationship between humans and robots, considering factors such as usability, safety, and social acceptance.

Technical expertise, while important in robot design and programming, is not directly related to human factors, which focus on understanding human capabilities and limitations in interacting with robots.

Robot transparency aims to keep users informed about the robot's current status, actions, and plans, enhancing trust and understanding in the human-robot relationship.

Affordance in HRI refers to designing robots with clear and intuitive cues that indicate their intended use and functionality, making them easier for humans to interact with.

A major ethical concern in healthcare robotics is the potential for robots to make errors or malfunctions that could result in harm to patients, highlighting the need for rigorous safety measures and testing.

Military robots, while used in various applications, are not typically considered part of HRI, which focuses on the interaction between humans and robots in civilian settings.

The end effector is the part of the robot that directly interacts with the environment, allowing it to grasp, move, or manipulate objects.

Temperature sensors, while used in various applications, are not typically considered essential for HRI, which primarily focuses on sensors that enable robots to perceive and interact with their surroundings.

NLP in HRI enables robots to understand and respond to human speech, facilitating natural and intuitive communication between humans and robots.

Social robots are primarily designed for interacting with humans in social settings, making them suitable for applications such as healthcare, education, and retail, but less commonly used in manufacturing environments.

ML in HRI allows robots to learn from human interactions, enabling them to acquire new skills and adapt to different situations without explicit programming.

While independent decision-making is a desirable feature for robots in some applications, it is not a key challenge in developing robots for effective collaboration with humans, which primarily focuses on communication, safety, and understanding human intentions.

Haptics in HRI involves the use of tactile feedback to enhance the user's experience during interaction with robots, providing a more natural and intuitive feel.

Flying locomotion, while being an active area of research, is not yet a widely adopted form of robot locomotion, unlike wheeled, legged, and swimming locomotion.

RL in HRI is primarily used to improve the robot's decision-making by allowing it to learn from its interactions with the environment and human users.