Why We Reject Robot Walls: New Model Reveals When People Accept Living Buildings That Move

Imagine arriving at your office one morning to find that the partition wall beside your desk has quietly relocated itself overnight, shielding you from the glare that has plagued your mornings for weeks. Now imagine the opposite scenario: the same wall glides across the room uninvited, blocking the window view you cherish and boxing you into a corner you never chose. Both events involve identical hardware and comparable engineering, yet they can produce opposite human reactions, from gratitude to outright hostility. This paradox sits at the heart of a new theoretical framework developed by an interdisciplinary team spanning Belgium, Germany, Italy, Slovenia and Israel, which asks a deceptively simple question: when do people actually accept a building that moves on its own?

The research, published in Frontiers in Computer Science, introduces the Architectural Robot Acceptance Model, or ARAM, a threshold-based model that explains how occupants respond to individual adaptations performed by roboticised building elements. The work addresses a growing tension in contemporary architecture and computing. Partitions, ceilings, desks, sofas and even beds can now physically change shape or relocate autonomously, promising gains in comfort, satisfaction and wellbeing. Yet occupants frequently override, avoid or attempt to disable such systems when adaptations feel disruptive, unpredictable or beyond their control. A single unacceptable action, prior human-robot interaction research suggests, can already damage human trust in a robot, making the timing and justification of each autonomous movement a high-stakes design decision.

To build the model, the team re-analyzed interview and observational data from three previous studies of robotic partitions, treating each documented adaptation as a unit of analysis. The three studies spanned markedly different contexts: a semi-immersive cross-reality simulation in eight private homes, a Wizard-of-Oz controlled physical partition in a semi-public office breakout space with 26 participants, and a five-week in-the-wild deployment of a semi-autonomous partition in a shared office with 13 occupants. Across these settings, occupants experienced a combined 247 adaptations, of which 125, or 50.6 percent, were sufficiently documented to enter the qualitative analysis. The re-analysis was conducted through an interpretivist reflexive thematic process involving ten co-authors with expertise in human-building interaction, human-robot interaction, environmental and social psychology, anthropology and human comfort, generating 42 and 36 initial codes from two complementary coding passes that were consolidated into 56 codes, 13 candidate thematic clusters and ultimately three themes.

Those three themes crystallized into three acceptance criteria. First, personal need: occupants had to recognize an unmet need that motivated the adaptation, whether reducing a disturbance such as glare, noise or lost privacy, or improving the quality of their spatial context. Second, adaptation capability: occupants had to expect the robot to actually address that need through its architectural and robotic affordances, an expectation shaped by past experience, colleague recommendations and perceptions of the partition’s size, speed and behavior. Third, social justification: occupants had to consider the adaptation defensible in the eyes of co-located others, weighing whether it disturbed colleagues, violated social norms, made them appear selfish or drew unwanted attention. The pattern that emerged was consistent: occupants tended to accept an adaptation when they recognized a personal need that the architectural robot was capable of addressing in a socially justifiable way.

Depending on how these criteria combined, occupants adopted one of four distinct responses. They endorsed an adaptation, engaging with it as intended, in 56 of the analyzed cases, or 44.8 percent, when all three criteria were met. They refrained from engaging in 38 cases, or 30.4 percent, when the adaptation seemed capable but socially unjustifiable, for instance because moving the partition would darken a colleague’s workspace or disturb an ongoing meeting. They repurposed an adaptation in nine cases, or 7.2 percent, using an otherwise incapable partition in unintended ways, such as redirecting it toward noisy colleagues to send a subtle behavioral message. They rejected an adaptation in 22 cases, or 17.6 percent, either because no personal need was recognized or because the adaptation appeared both incapable and socially indefensible. One participant who rejected an adaptation described feeling threatened as the partition approached; another abandoned a desk that had become claustrophobic when the partition closed in too near.

The conceptual leap of ARAM lies in how it organizes these criteria. Rather than treating personal need, capability and social justification as independent variables, the model positions them as three qualitative thresholds along a single continuum of perceived stress severity, meaning how consequential an unmet or anticipated need becomes for an occupant in a particular situation. Personal need and social justification act as lower thresholds: as perceived stress rises, an unrecognized need becomes recognized, and an unjustifiable adaptation becomes justifiable. Adaptation capability, by contrast, acts as an upper threshold: beyond a certain severity, an adaptation once deemed capable may become insufficient, for example when a partition is too small to block intense glare or too slow to respond in time. The relative positions of these thresholds, compared against the occupant’s current stress level, determine which of the four responses becomes possible.

The model is explicitly situated rather than fixed. Threshold positions vary across occupants, adaptations, needs and contexts, and can shift for the same person over time. An occupant who prioritizes social harmony in an unfamiliar setting may consider an adaptation justifiable only at high stress levels, refraining from it at lower levels even when it addresses a genuine need; in a familiar context, the same person may accept it far earlier, trusting that colleagues will understand its purpose. The authors ground this threshold logic in established psychological theory: the transactional model of stress and coping, which frames stress as a product of subjective appraisal; protection motivation theory, which links responses to perceived efficacy of protective actions; and Granovetter’s threshold model of collective behavior, which defines the point at which perceived benefits of acting outweigh social costs.

ARAM also departs meaningfully from established technology acceptance frameworks such as the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology. Those models explain adoption at the level of the overall system through constructs like perceived usefulness, effort expectancy and social influence. ARAM instead operates at the level of the individual, situated adaptation, distinguishing responses that conventional models collapse into a single intention-to-use measure. An architectural robot may be perceived as useful and easy to use in general, yet a specific adaptation may still be judged incapable of addressing a particular need at a particular moment. Conversely, an adaptation may be perceived as capable even when its measurable technical performance is limited, if occupants believe it meaningfully reconfigures their space. The authors propose two testable propositions: that independently assessed combinations of the three appraisal states should systematically correspond to the four responses, and that increasing perceived stress severity should produce the ordered sequence of responses implied by an independently established threshold order.

The practical implications extend to how architectural robots might one day estimate acceptance in real time. The authors suggest that personal need could be proxied through sensing of visual, acoustic, thermal and air-quality conditions, alongside spatial factors such as occupancy density and visual exposure, triangulated with occupant feedback and Personal Comfort Models. Adaptation capability could be estimated through onboarding studies, interaction logs and post-adaptation feedback, with demonstrations helping occupants form accurate expectations of what a robot can and cannot do. Social justification could be informed by occupants’ familiarity with co-located others, perceived norms, hierarchy and sense of responsibility. Where reliable estimates exist, a robot could prioritize adaptations likely to be endorsed, delay those likely to be rejected, request confirmation when occupants may refrain, or offer alternatives when repurposing seems probable. The authors also caution against maximizing predictability at all costs, noting that excessive predictability could erode the curiosity and serendipity that unexpected robotic behavior can inspire.

The researchers are careful to scope their claims. ARAM was developed from robotic partitions operating at the architectural space-plan layer, and its transferability to other robotic, automated, intelligent and smart technologies embedded in the built environment remains to be evaluated, particularly for systems differing in materiality, autonomy or social visibility. The qualitative methodology relied on self-reported experiences subject to recall effects, and the model has not yet been independently validated with data collected specifically to test its propositions. Real-time threshold estimation would also raise technical challenges alongside questions of privacy, consent and the appropriateness of continuous behavioral and physiological sensing. Still, the framework offers designers and researchers a common analytical lens for a future in which walls, ceilings and furniture are no longer static backdrops but active agents whose every movement must earn its place in the shared social fabric of indoor life.

Subject of Research: A threshold model of occupant acceptance of architectural robot adaptations based on qualitative re-analysis of three robotic partition studies

Article Title: ARAM: a threshold model for understanding occupant acceptance of architectural robot adaptations

Article References: Nguyen, A. B. V. D., Christoforou, R., Pigliautile, I., Martins Gnecco, V., Pogladič, A., Arko, S., Pisello, A. L., Schaumann, D., Schweiker, M., & Vande Moere, A. (2026). ARAM: a threshold model for understanding occupant acceptance of architectural robot adaptations. Frontiers in Computer Science, 8, Article 1896307. https://doi.org/10.3389/fcomp.2026.1896307

Image Credits: AI Generated

DOI: 10.3389/fcomp.2026.1896307

Keywords: architectural robots, human-robot interaction, human-building interaction, technology acceptance, adaptive architecture, occupant comfort, robotic partitions, environmental psychology, social justification, perceived stress, smart buildings, ARAM model