Diffusive Habitats Iṣẹ́ Pàtàkì: Ètò Ilé-Ìkọ́lé Tí A Lè Ṣàtúntò láti ọwọ́ Sergio Mutis

Diffusive Habitats Iṣẹ́ Pàtàkì: Ètò Ilé-Ìkọ́lé Tí A Lè Ṣàtúntò láti ọwọ́ Sergio Mutis jẹ́ aṣẹ́gun olókìkí púpọ̀ ti 2023 Àmì-ẹ̀yẹ Idẹ A' Design Award nínú Ẹ̀ka Ìdíje Àpẹẹrẹ Ìpilẹ̀ṣẹ̀, Àlgọ́rídímù, Párámétrìkì àti Àtìlẹ́yìn-AI.

IdẹÌpilẹ̀ṣẹ̀· 2023

Diffusive Habitats Iṣẹ́ Pàtàkì: Ètò Ilé-Ìkọ́lé Tí A Lè Ṣàtúntò

látọ̀wọ́ Sergio Mutis

Aṣẹ́gun Àmì-ẹ̀yẹ Idẹ A' Design Award nínú Ẹ̀ka Ìpilẹ̀ṣẹ̀

78,281

Aṣẹ́gun Àmì Ẹ̀yẹ Tí a Jẹ́rìísí

Jẹ́rìísí Ipò Àmì Ẹ̀yẹ fún #145846

Ṣàbẹ̀wò Ojú Òpó Wo ní A' Design Award Ìwé Ìròyìn Àmì Ẹ̀yẹ

Àwọ̀

Àpẹrẹ Náà

Nípa Diffusive Habitats Iṣẹ́ Pàtàkì: Ètò Ilé-Ìkọ́lé Tí A Lè Ṣàtúntò

Ọ̀rọ̀ Ìwé: Diffusive Habitats jẹ́ ètò ilé-ìkọ́lé kan tí ó so àwọn róbọ́ọ̀tì tí a pín kálẹ̀ (distributed robotics), ẹ̀kọ́ amúnisọ̀rọ̀ (reinforcement learning) àti pẹpẹ orí-ayélujára pọ̀ láti jẹ́ kí a lè ṣàtúntò àwọn àyè ní àkókò dídànrán. Àwọn ẹ̀rọ róbọ́ọ̀tì máa ń gbé àwọn ẹ̀yà módù kiri láti yí ìṣètò ibùgbé pa dà. Àwọn ẹ̀yà módù wọ̀nyí ni a ṣe láti inú plywood tí a fi CNC gé tí ó sì lẹ̀ pọ̀ (laminated), tí a sì fi àwọn ojú-ìsopọ̀ tí a tẹ̀ jáde ní 3D bọ sínú rẹ̀. Àwọn àwòṣe róbọ́ọ̀tì náà ni a tẹ̀ jáde ní 3D pẹ̀lú pilasitiki tí a tún lò. Pẹpẹ náà ń ṣètò àyè náà gẹ́gẹ́ bí àìní àwọn olùgbé, ìdúró-ṣinṣin ìkọ́lé àti bí ó ṣe ń bá àyíká mu. Èròngbà náà ni láti dín ìwó-ilé kù àti láti jẹ́ kí àwọn ibùgbé lè yí padà ní ìrọ̀rùn.

Ìdánimọ̀ Náà

Nípa Àmì-ẹ̀yẹ Idẹ A' Design Award

Diffusive Habitats Iṣẹ́ Pàtàkì: Ètò Ilé-Ìkọ́lé Tí A Lè Ṣàtúntò jẹ́ olùborí Àmì-ẹ̀yẹ Idẹ A' Design Award, ìdánimọ̀ olókìkí gan-an tí a fún àwọn àwòṣe tó tayọ tí wọ́n yàtọ̀ nípasẹ̀ ìṣẹ̀dá, àrékérekè àti ọgbọ́n inú. Àwọn olùborí Idẹ jẹ́ àwọn ohun èlò tó ní ìjáfáfá gan-an tí wọ́n sì ní ìṣẹ̀dá tó yani lẹ́nu tí wọ́n ń ṣe ẹ̀rí ìrírí gbígbòòrò, ìrònú àti ọgbọ́n àwọn olùṣàwòṣe wọn. A retí pé Diffusive Habitats Iṣẹ́ Pàtàkì: Ètò Ilé-Ìkọ́lé Tí A Lè Ṣàtúntò yóò jẹ́ ohun tí a kà sí gidigidi, tí a gbóríyìn fún tí a sì nífẹ̀ẹ́, tó jẹ́ ti ọ̀jáfáfá lọ́nà tó ṣe pàtàkì tó sì ní ìmúṣẹ̀dá tuntun. Àwòṣe tó borí Àmì-ẹ̀yẹ Idẹ A' Design Award yìí ń kó àwọn ọ̀nà tó dára jùlọ nínú iṣẹ́ ọnà, sáyẹ́ǹsì, àwòṣe àti ìmọ̀-ẹ̀rọ jọ, ó sì tayọ nítorí àfiyèsí rẹ̀ sí àwọn àlàyé kékeré. Diffusive Habitats Iṣẹ́ Pàtàkì: Ètò Ilé-Ìkọ́lé Tí A Lè Ṣàtúntò ń tẹ́ àwọn ohun tí a nílò lọ́rùn lọ́nà àgbàyanu, ó ń fọwọ́ kan àwọn ìfẹ́ inú, ó ń gbà ọkàn ó sì ń fa ìfẹ́ láti mọ̀ mọ́ra.

Lẹ́yìn Àpẹrẹ Náà

Àkójọ Ìròyìn

Àwọn ẹ̀tọ́, àwọn ọmọ ẹgbẹ́ àti àlàyé ìmọ̀ ẹ̀rọ tí olùṣàpẹrẹ pèsè.

Àwọn Ohun Àkànṣe: Diffusive Habitats (DH) is a resilient architectural system that builds upon the juncture of distributed robotics, reinforcement learning, and digital platforms to reimagine the way architecture is conceived and inhabited. Challenging traditional praxis, the project embraces constant spatial reconfiguration and distributed ownership. And, via the interplay of applied transdisciplinary research, DH critically explores a spatial system capable of self-assessment, organization, and improvement.
Ìwọ̀n àti Àpò: Robot Prototypes 3.0.
Dimensions: Width 100mm x Depth 100mm x Height 280mm.
Composition: 3D-Printed recycled plastic (PLA). Weight: 420g. Parts: 5 links of 3D-printed PLA, 8 neodymium magnets 20dx3, 4 Dynamixel AX-12A motors, 1 LiPo 3cell Battery, 1 U2D2 Power Hub, 1 Raspberry Pi Zero.

// Material Prototypes 3.0.
Standard Dimensions: Width 140mm x Depth 420mm x Height 140mm. Standard Weight: 477g.
Composition: CNC-milled laminated plywood with embedded 3D-printed PLA connection interfaces.
Àwọn Àkọlé: Living Architecture, Distributed Mechatronics, Reinforcement Learning, Multi-Agent Systems, Generative Architecture
Ìmọ̀-ẹ̀rọ Ìṣelọ́pọ̀: The DH system consists of multiple correlated elements that were heuristically prototyped, tested, and redesigned. Its space generation platform was scripted in C-sharp for Unity3D with the ML Agents U3D reinforcement learning framework. Laminated plywood prototypes were CNC-milled and assembled with friction joints. 3D-printed PLA robotic prototypes were developed with a Unity3D digital twin environment, wireless Raspberry Pi communication, Dynamixel AX-12A motors, and Optitrack motion capture.
Ìpèníjà Àpèsè: Besides technological and transdisciplinary challenges, the shift in authorship paradigm was perhaps the greatest obstacle in the development of the project. Rather than the desires of individual designers, this research required a heuristic process of trial and error, where the design was the result of cascading iterations of parameter optimization. Once the team embraced that fundamental idea, the once slow and erratic project advanced swiftly beyond its initial research objectives.
Ìwádìí: Diffusive Habitats employs systematic heuristic research, with reinforcement learning and self-play frameworks for intelligence development. The main research objective was to explore an architectural system capable of self-organization, assessment, and improvement, that would embrace technological advancements to reimagine how we conceive and inhabit spaces. To do so, the project adopted a method of small attainable mechatronic, computational, material, and intelligence milestones that were quickly designed, tested, and analyzed to inform an upcoming iteration. Data collection and analysis included: weight, task success rate, and cost for robotic design / multi-requirement satisfaction for computational generative design / weight, open source fabrication index, and sustainability for material research / task success rate, speed, and energetic efficiency for robotic intelligence. Tests were carried out via simulations in Unity and C# for computational algorithms and robotic intelligence, and via physical tests for mechatronic and material development. Initial results were systematically improved by following iterations leading up to a mechatronic design with diverse capabilities (particularly while collaborating), a computational framework that could organize space under numerous requirements far better than any one designer, a material unit that proved capable of forming multiform structures (though not yet capable of being employed in buildings over 2 stories high), and a robotic intelligence that could calculate, optimize, and execute large reconfiguration tasks.

The project still requires development to be thoroughly applied in architectural practice. Yet, within the constraints of a 1-year Master's degree project, it was considered a vast success and will be used to attract interest and funds to further the research. The effects of this investigation range from avoiding demolition and boosting the sustainability of the built environment, to transforming rigid urban dwellings into flexible hubs for the ever-accelerating change of our times. Altogether, this research explores a radically novel way of living that could be a widespread reality in the future, or allow for new conversations to occur that will ultimately improve the way we live.
Ìmísí: Unlike other crafts, arts, and industries, architecture has not seen significant developments during the upheaval of the digital revolution. Traditional praxis continues to produce buildings incapable of substantial programmatic flexibility, that become obsolete within the ever-accelerating change of the times, and quickly end up in queue for demolition. Diffusive Habitats challenges this unsustainable practice, instrumentalizing its technological juncture in the lookout for renewed solutions.
Àkókò Iṣẹ́ Àkànṣe: The project was developed as part of the Architectural Design M.Arch. at UCL in London, UK. It started in 2021 and culminated in late 2022 at Bartlett B-Pro Show Expo, where it received the Bartlett AD Gold Medal. The case-study is located in Brixton, London, and the design team comes from India, Colombia, the US, and Greece.
Ìlànà Ìṣiṣẹ́: Diffusive Habitats operates via an online platform facilitating plot search, habitat crowdfunding, collaborative design, and spatial reconfiguration. Behind its intuitive interface, the platform employs sophisticated algorithms to organize space based on inhabitant requirements, structural integrity, and environmental optimization. With it, a community is able to design their ideal habitat, see it constructed by robots, and adjust it to account for changing individual and collective needs.
Àwọn Ọmọ Ẹgbẹ́: Sergio Mutis
Eric Hughes
Faizunsha Ibrahim Ghousiaa
Garyfallia Papoutsi.
Ìfọwọ́sí Àwòrán: Image #1: Creator Eric Hughes, DH Little Girl Rendering, 2022
Image #2: Creator Sergio Mutis, DH Reinforcement Training Arena, 2022
Image #3: Creator Eric Hughes, DH Skylight Rendering, 2022
Image #4: Creator Sergio Mutis, DH Robotic Reconfiguration Studies, 2022
Image #5: Creator Eric Hughes, DH Speculative Urbanism, 2022