SpotMini is ‘a nimble robot that handles objects, climbs stairs, and will operate in offices, homes and outdoors,’ reads the website of developer Boston Dynamics. Boston Dynamics is a commercial spin-off from research conducted by founder and CEO Marc Raibert at MIT in the Nineties into quadruped robots. Its dog-like robot Spot and its domestic pup SpotMini have over the past four years amassed millions of views on YouTube, where videos show them climbing stairs, dancing, pulling heavy objects, and walking in the woods. A video showing SpotMini opening a door while thwarted by a hockey stick-wielding engineer is what first drew Niquille’s attention.

‘The videos strike a peculiar tone between slapstick comedy, meme-worthy content and engineering test footage,’ Niquille writes in an essay based on her initial visual research, adding that ‘seeing a robot fail is amusing, yet carries an uncanny undertone’. The machine vision algorithms of robots are trained according to datasets of what domestic and office spaces and their contents should conform to. These ‘shoulds’ can be traced back to the standardising models that range from formal standards such as the 15th-century origins of the mesh grid used to digitally model 3D space and 20th-century furniture sizes, to how the personal and pragmatic biases of the engineers influence their notion of the typical habits and routines of humans.

Regarding The Pain of SpotMini / HOMESCHOOL by Simone C Niquille, 2018-ongoing. Installation view during Copenhagen Architecture Festival CAFx 2019.
‘An autonomous robot’s struggle to navigate spaces designed for humans reveals the designer’s assumptions regarding the intended user (and the robot’s failure at imitating them). Once functioning flawlessly, these assumptions causing violent consequence not only for the robot but, more importantly, for the excluded user, are hermetically sealed within automation,’ Niquille says, using the 2015 case of a woman attacked by a Roomba autonomous vacuum cleaner to illustrate these consequences. While the public outcry echoes the fears of the mythical robot apocalypse, what is really at stake according to Niquille, is that the woman was not behaving in the way that the Roomba had been trained: she was sleeping on the floor, not on a bed; during the day, when she should have been at work.

Never mind what is a chair, how does a robot know what is a human? When attempting to quantify the complexity of human behaviour in sets of 0s and 1s, glitches are inevitable. The algorithm training decisions informed by inevitably incomplete models of human behaviour could come to imprison our behaviour in conforming to models or have violent consequences. ‘It’s never going to be a model of reality, but certainly is a model of someone’s reality, be that the engineer who puts together the training database or be it a model of what SketchUp or other databases of domestic environments contain,’ Niquille explains.

Regarding The Pain of SpotMini / HOMESCHOOL by Simone C Niquille, 2018-ongoing.
Such conformism also advances particular commercial interests. For instance, through her design research into what the training database of SpotMini might be, Niquille identified that the majority of furniture was from IKEA. Training machines to navigate domestic spaces, which have a far higher degree of variability in furniture form and spatial layout than offices, is a lot trickier. Thus the relevance of what a chair is: ‘Are chairs furniture with four legs and back rests, or any object for sitting?’ The answer can not only determine the degree of safety with which autonomous robots navigate spaces originally designed for humans, but predict the form that future spaces, designed around human and robot cohabitation, might take, and which commercial partners get to decide.

It is these implications that motivate Niquille’s design research practice, which seeks to ‘make visible the thing that is always invisible once you’ve created a computer vision algorithm – the training data’. First studying graphic design at Rhode Island School of Design before completing a Master’s in Visual Strategies at Sandberg Instituut, Niquille’s methodology uses available online data to reconstruct and thereby reverse engineer the original dataset. ‘These constructed images embedded in training databases become “objective reality” and the model for domesticity, even though they can’t be because we can never amass enough data to encompass the complexity of real life.’

Regarding The Pain of SpotMini / HOMESCHOOL by Simone C Niquille, 2018-ongoing.
Regarding The Pain of SpotMini / HOMESCHOOL by Simone C Niquille, 2018-ongoing. Installation view during Copenhagen Architecture Festival CAFx 2019.
After reverse engineering the Boston Dynamics test house using SpotMini videos and interviews with the developers, Niquille’s research expanded to similar databases that model human spaces such as the CNET database, which is ‘comprised of around 100,000 3D objects and a bunch of different room layouts, organised in categories such as living room, bedroom, office and kitchen’. Using these layouts and objects as assets, Niquille is designing a film character that sparks reflection and discussion about whether we humans design our technology or our technology designs us.

technofle.sh

housingthehuman.com



Housing the Human Festival, Radialsystem, Berlin, 18 - 20 October



Simone C Niquille