Have you ever seen a face in the headlights of a car or a surprised expression in a power outlet? This phenomenon is called pareidolia—our brain’s instinct to recognize faces in inanimate objects. While originally an evolutionary survival mechanism, modern designers leverage pareidolia to enhance user experience, particularly in technology and robotics. In warehouse industry settings, Autonomous Mobile Robots (AMRs) can use pareidolia to improve user trust, interaction, and efficiency.  

Pareidolia in the Design of Warehouse Robots (AMRs)  

Perception of Curves  

The shape of an AMR significantly influences human perception and interaction. Studies by Bar & Neta (2006) show that humans are naturally drawn to curves, as they evoke feelings of comfort and safety. Research suggests that people instinctively favor curved designs over sharp angles, associating them with warmth and security. In AMRs, curved edges and smooth contours can make robots seem friendlier, encouraging positive user interaction. For example, a warehouse worker may feel more at ease around an AMR with a rounded body rather than one with sharp, rigid lines, as curves are subconsciously linked to non-threatening, organic forms.  

Perception of Forms  

Pareidolia extends beyond curves to how we perceive arrangements of features. Placing sensors, cameras, or indicator lights in a way that resembles a face can unconsciously make the robot feel more relatable. Face-like sensor placements, subtle LED ‘eyes,’ or even positioning screens as a ‘mouth’ can enhance human-robot communication. This strategy increases engagement and trust, helping workers feel more comfortable with robots in their environment.  

Strategies for Using Pareidolic Elements in AMRs  

To optimize pareidolia in AMRs without creating unintended effects, designers should consider:  

• Minimal Anthropomorphic Cues: Subtle facial elements, such as evenly spaced sensors, can make AMRs appear more predictable without making them overly human-like.  

• Function-Driven Features: Using familiar facial arrangements should serve a practical purpose, such as aiding communication with lights that indicate operational status.  

• Avoiding the Uncanny Valley: Overuse of human-like traits may make robots unsettling rather than inviting, so a balance is necessary.  

Factors Influencing Pareidolia

Pareidolia isn’t just a trick of the imagination; it’s influenced by multiple psychological and environmental factors:  

Brain Wiring & Evolution: Humans are hardwired to detect faces, aiding quick recognition.  

• Lighting & Shadows: Soft contrasts can create illusions that resemble human features. 

•  Symmetry & Shape: Certain feature arrangements, like two sensors and a central indicator, can mimic facial expressions. 

•  Cognitive State: Stress, loneliness, and mood influence how likely a person is to experience pareidolia. 

•  Culture & Experience: A person’s background affects which patterns they recognize as familiar.  Age & Development: Younger and older individuals are more prone to pareidolia. 

•  Motion & Perspective: A robot’s angle and movement can enhance or diminish the perception of a face.   

Experimental study 

An experiment was conducted where participants were shown front-view images of robots and asked what age, gender, and emotions they felt the robots conveyed. This is what the majority of participants felt.  

Pareidolia in AMRs is a double-edged sword—it can improve user interaction and trust but may also create misleading impressions about a robot’s capabilities. Pros include: enhanced emotional connection, increased trust, and intuitive usability. Cons involve: potential misinterpretation of the robot’s intent and unrealistic user expectations.  

To use pareidolia effectively in product design, AMR designers should focus on subtle, function-driven facial cues that enhance usability without over humanizing the machine. By leveraging natural human tendencies, designers can create AMRs that not only perform efficiently but also integrate seamlessly into human work environments.