Crossing Paths: When Human-Centered Frameworks Are No Longer Enough
By Shreya Mathur (MDes 2026) and Claude Sonnet 4.5
January 29, 2026
Summary
Autonomous delivery robots are already navigating city sidewalks alongside pedestrians, often without clear social norms or shared expectations. Shreya Mathur (MDes 2026), working with Associate Professor Ruth Schmidt, examined how people develop trust in these systems through everyday encounters in cities from Chicago to Bengaluru. The resulting design framework translates trust—an abstract concept—into concrete behavioral design decisions through three core experiential constructs: Collaborative Ease, Intent Clarity, and Emotional Comfort.
While focused on delivery robots, the framework’s principles extend to any domain where autonomous systems share space with humans, from hospital service robots to home assistants.
A Design-Led Exploration of Public Space Interaction
As autonomous products move into everyday life, they now share public space with people, navigating the same sidewalks and intersections. While these robots often spark curiosity, they can just as easily cause frustration or discomfort.
This project began with a simple provocation: what happens when the user is no longer human? When both humans and autonomous systems are primary users of the same space, traditional human-centered frameworks are no longer enough.
OPPORTUNITY
From Pilot Programs to Public Infrastructure
Companies like DoorDash have launched their own delivery robots (Dot) as recently as September 2025, and UberEats continually expands its robot partnerships across cities. We’re at an inflection point—transitioning from pilot programs to defining infrastructure.
Technical autonomy is progressing rapidly, with robots becoming smarter and more capable. However, the relational layer—how humans interpret robotic behavior and develop trust—remains largely unexplored. This imbalance highlights a critical design gap.
PROCESS
From Exploration to Framework
Rather than following a linear process, the project moved between exploration, synthesis, and reframing before reaching the design stage. The work began by examining how technology has shaped human life and exploring the evolving meaning of autonomy, then gradually narrowed to focus on building trust in public space.
Research Methods
Photo by JIP via CC BY-SA 4.0
Examined peer-reviewed research on human-robot interaction design, consumer attitudes toward autonomous delivery, behavioral factors influencing adoption, and technical dimensions of sidewalk robot deployment. This provided theoretical foundations for understanding what makes autonomous systems legible and trustworthy.
Analyzed posts, comments, and videos to surface spontaneous emotional reactions, informal social norms, real-world behaviors, and emerging patterns in public expectations.
Applied Anatomy of Infrastructures (Andre Nogueira) and Behavioral Systems 3×3 (Ruth Schmidt) as sensemaking frameworks to analyze complex systems and relational dynamics.
Designed and deployed to 30 participants aged 25-40 across seven global cities (Chicago, New York, Bangalore, San Francisco, Toronto, Madrid, Delhi) to ground theoretical constructs in lived responses.
Systematic analysis of questionnaire responses identified recurring patterns and validated the three experiential constructs.
Visual exploration of interaction scenarios communicated key moments in human-robot encounters and behavioral patterns.
OUTCOME
A Relational Chain of Trust
The framework centers on a key concept: the relational chain of trust. The relational chain of trust shows how designer intent becomes robot behavior, which shapes human perception and accumulates into a relationship over time. This progression makes trust visible as a design variable rather than a hoped-for outcome.
The framework operationalizes trust through three human experience constructs, each translated into actionable design principles and specific, observable behaviors:
- Collaborative Ease
- Intent Clarity
- Emotional Comfort
See the framework and a description of each of these constructs below.
Collaborative Ease addresses how effortlessly humans and robots coordinate in shared space.
Key finding: 70% of questionnaire participants favored robots that explicitly communicated distress (“Please help! I can’t move”) during failure moments, while preferring quiet, non-intrusive behavior during normal operation.
Intent Clarity addresses how transparently a robot communicates its purpose, direction, and state.
Key finding: 56% of participants selected “the robot clearly displaying what it’s doing” as most important—reinforcing Intent Clarity as a primary driver of comfort and cooperation.
Emotional Comfort centers on how safe and at ease people feel in the robot’s presence.
Key finding: 64% preferred robots using simple, polite motion cues over expressive personality, suggesting that calm signaling beats constant emotiveness for comfort.
IMPACT
From Delivery Robots to Broader Applications
The framework provides immediate value for teams working on delivery robots by helping them identify sources of friction and make intentional design decisions that shape emerging sidewalk norms. It can be applied across multiple development stages—from early concept exploration to prototyping and testing.
Beyond delivery robots, the framework has broader relevance for:
- UX and Human-Robot Interaction Designers shaping behaviors and communication methods,
- Product Teams defining behavior specifications, and
- Company Leadership making strategic decisions about where, when, and how anthropomorphized or autonomous technology is introduced into public environments.
The framework extends to any domain where autonomous or anthropomorphized systems coexist with people—service robots in hospitals, warehouses, or campuses, as well as consumer-facing autonomous products like robotic pets or home assistants operating in semi-public or shared spaces.
TEAM
Shreya Mathur (MDes 2026)
Ruth Schmidt, Associate Professor of Behavioral Design