Machine Learning in Design and Assembly Process of the Autonomous Habitat by Nguyen Xuan Man

MACHINE LEARNING IN DESIGN AND ASSEMBLY PROCESS OF THE AUTONOMOUS HABITAT

NGUYEN XUAN MAN 17109862

THE BARTLETT SCHOOL OF ARCHITECTURE MARCH ARCHITECTURAL DESIGN RESEARCH CLUSTER 4 THEORY TUTOR MOLLIE CLAYPOOL DESIGN TUTOR GILLES RETSIN MANUEL JIMENEZ VICENTE SOLER

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MAch Architecture Design The Bartlett School of Architecture 22 Gordon Street, London, WC1H 0AJ

RESEARCH CLUSTER 4

Theory Tutor : Mollie Claypool Date of submission : 13 July 2018

Nguyen Xuan Man 17109862 ucqbmxn@ucl.ac.uk

Word count: 8083

Pizza Box

KEYWORD Machine Learning, Computational Design, Robotic Assembly, Digital Material, Artificial Neural Network, Cybernetic, Embodied Cognition, Distributed Production, Automated Habitat, Assemble Assembler.

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Pizza Bot

ABSTRACT This thesis will focus on the development and application of Machine Learning on the design and construction of the autonomous habitat. The aim of the design project is to question autonomous process in architecture and construction, mainly focus on the robotic assembly process to build a fully automated habitat. The computational task within the design project is to develop Machine Learning algorithms for on-site construction robots which are geometrically identical with the modular building components, operating on a three dimensional grid and aggregate a discrete reconfigurable structure (M4G, 2018). The Machine Learning algorithm with the main task as to train the control system of the robots will also formulate constraints and guidelines for the generative design process and on-site construction execution. Hence, the architects who have the knowledge of design house and home, should be the creator of the algorithms to digitalize the making process of inhabitable spaces (see for example Cross, 1977; Frazer, 1995; Oosterhuis, 2003). The main source of reference are emerged from philosophical approach of perception, behavioural and cybernetic in architectural design, to technological research in AI development and robotic control system for virtual simulation and on-site self-adjustment to function with real construction limitation.

NCCR Digital Fabrication ETH Zurich

WYSS Institute TERMES

Figure 1: Comparison between industrial robotic arm (global robot) with non-digital material and multiple small climbing robot (relative robot) assembling digital material. (Image: Gramazio Kohler Research, 2011, Werfel, et al., 2014) Figure 2: Proposal for an integrated system between building material and robotic technology, and questioning the design process within this system.

SEPARATE SYSTEM

INTERGRATED SYSTEM

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CONTENT 1. Introduction

2. Machine Learning Ideology 12

2.1 General classification 2.2 Exo-brain & Exo-body 2.3 Cybernetic

14 15 18

3. Machine Learning in Design Process:

3.1 Rethinking the Design Process 3.2 Evolutionary Structure Generation 3.3 Data Categorization 3.4 Spatial Arrangement Optimization

22 24 26 28

4. Machine Learning in Assembly Process:

4.1 Rethinking the Modern Factory 4.2 Locomotion Neural Network 4.3 Swarm Autonomous Assembly 4.4 Real time decision-making algorithms

32 34 38 42

5. Conclusion and Outlook

6. Reference and List of Figures

01 8

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INTRODUCTION

Figure 3: AlphaGo Deep Learning Algorithms - wining human opponent by â&#x20AC;&#x153;artificial intuitionâ&#x20AC;? (Image: DeepMind, 2018)

Figure 4: Atlas use machine learning for locomotion system, and performing a backflip like a gymnastic (Image: Boston Dynamic, 2017)

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On March 9th, 2016, AlphaGo defeated world class player Lee Sedol at Go, the most complex game human ever created which has “more possible move than there are atoms in the universe” (DeepMind, 2018). By developing a sort of intuition through a machine learning algorithm, AlphaGo was able to overcome the challenge that tradition logic-based Artificial Intelligent fall to achieve. (DeepMind, 2018). Not long before that, Atlas the humanoid robot, by training through machine learning algorithm in the course of half a year, is not only learned to walk and run, but also perform a back flip like a gymnastic (Boston Dynamic, 2017). Those two novel achievements are broadcasting solid evidence proving the exponential development of Artificial Intelligent and Robotic field. Machine is gaining the ability to use intuition and performing embodied cognition, which was often classify as human’s fundamental device on the creative task. Hence, the appropriation that architect and the designer should embrace the power of Machine Learning in the creation process of our built environment is becoming more relevant than ever. Based on the research from MIT Centre for Bit and Atom on ‘Digital Material’ and ‘Autonomous Assembly” (Gershenfeld, et al., 2015), this paper will further research on how machine learning could enhance the idea of discrete aggregation interwoven with robotic assembly in architecture. The autonomous robotic assembly as the goal for automation in construction could not be achieved without a control system which base on a feedback loops training process to achieve complex locomotion and collaboration. The design project aim to develop Machine Learning algorithms for multiple climbing on-site robot which are geometrically identical with the building modular component, occupy on a grid system and aggregating a discrete reconfigurable structure. The algorithm with the main task to train the control system of the robots will also formulate constraints and guidelines for the design process and assembly sequence. Feedback-base computational algorithms play a central part in the design process of the project. Start with a combinatorial logic of the tile, digital input dataset was generated for computational aggregation. Then, path-finding and collision checking algorithms were applied for the system of control for the locomotion and assembly sequence of the robots. Furthermore, the aggregated structure was categorised and optimized through machine learning process, generating the optimum structure. The narrative of this thesis will combine two interwoven frameworks: theoretical approach and technical speculation, to address the question of how to apply machine learning into the specific brief of the autonomous habitat. The technical innovation part will focus on the case studies of the use of generative machine learning system in design, simulation and real – time control system of the robot. The theoretical part will cover the general ideology of machine learning, perception and cybernetic in architecture, and the typological influences of computation and robotic technology to the spatial configuration of the house. “Machine learning is accelerating the progress of generative design and robotics, transforming the way things are designed and made” (Medium, 2018).

02 12

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MACHINE LEARNING IDEOLOGY

To apply machine learning into architecture, the basic ideology of machine learning and its relevant context will be explored in this chapter. Machine learning general concepts and subdivision are explained in the first part. A comparison between human learning and machine learning will be built up to support the hypothesis that machine as an extension of human body and mind is gaining its independence in performing creative task. Finally the relation between machine learning and cybernetic theory in architecture is compared to ground the focus of this design thesis.

Figure 5: Machine Learning Categorization & Brief History. ML is a smaller subsets of Artificial Intelligence, and Deep Learning is a branch of Machine learning(Image: Bisintek, 2011).

Figure 6: Alpha Go deep learning neural network, model as an exo-brain for humman (Image: DeepMind, 2018).

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2.1 General Classification Machine Learning (ML) is a sub field of Artificial Intelligence (AI) science, first coined by Arthur Samuel in 1959, defined as giving the computer systems the ability to “learn without being explicitly programmed” (Samuel,1959). Rather than giving specific instructions by hard coding software command, Machine Learning is a method of “training” an algorithm so that it can learn how to progressively improve performance on a specific task with data. By providing large amounts of dataset, the “training” process would allow the algorithm to re-adjust itself and evolve (Samuel, 1959). Machine Learning algorithms come in three main divisions: Supervised Learning, Unsupervised Learning and Reinforcement Learning (Marsland, 2014). Supervised Learning is defined as the computer is given example inputs and target outputs by a supervisor, and the goal is to learn the rule that connect inputs to the outputs correctly. With Unsupervised Learning, unlabelled data are feed to the learning algorithm, and the objective are to discovering hidden patterns in data, the structure of the inputs or a means towards an end of feature learning. And Reinforcement Learning is a training method where software agents learn to adjust their actions in an environment to achieved higher reward after each generation (Marsland, 2014). The most advance in Machine Learning development recently are Deep Learning, which base on building and an artificial neural network which resemble the human brain system. This method showing revolutionary result for complicated task like playing intricate game, as computer learning to master the game by playing it millions of times. Beating the best human in the Go game, this artificial neural network was described by Lee Sedol as a “different kind of intelligent” (DeepMind, 2018). The question of how we apply this type of intelligent in architecture is the broader agenda for this thesis objective, and an inevitable challenge for the next generation of architects in the digital age. In this design thesis, unsupervised learning is utilized for data clustering from thousands of designs iteration, and reinforcement learning are using to train the locomotion of the robot to cope with unfamiliar built environment.

2.2 Exo-brain & Exo-body To answer the basic question that what is the application machine learning in architecture, the fundamental ideology of human learning in architecture need to be examined in a philosophical approach. Fundamental ideology of machine learning could be traced back from the begin of computation with the Alan Turing’s test on a human-cognitive machine, a “machine that can learn from experience” (Turing ,1950). However, Turing suggested that “the best strategy for the machine may possibly be something other than imitation of the behaviour of a man”. He emphasised that the “nervous system is certainly not a discrete-state machine”, and “given a discrete-state machine it should certainly be possible to discover by observation sufficient about it to predict its future behaviour”. (Turing, 1950). W. Grey Walter, on his research on ‘A Machine that Learns’, stated that “the mechanism of learning is of course one of the most enthralling and baffling mysteries in the field of biology” (Walter, 1951). His experiment on the Machina Speculatrix, by simple mechanical impulse mimicking the very basic function of brain and learning, was “resembling in some ways the

Figure 7: Machina speculatrix by Water W.Grey. The robot nicknamed "tortoise", due to its appearance had three wheels, each one with an independent motor. It's an extremely simple circuit, but that was able to generate complex and unpredictable behaviour as its sensors interacted with the environment. (Image: Elsie, 1950) Figure 8: Michael Silverâ&#x20AC;&#x2122;s OSCR-4 prototype working on different construction sites, act as an exo-body for the human worker ( Image: SMART, 2016)

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random variability, or free will, of and animal’s response to stimuli”. (Walter, 1951). Those early experimental of machine learning built up the foundation for the exploration on the process from human perception and machine sensory input to the behaviour and interaction as the output. The significant part of human learnings including remembering, adapting and generalising , so by recognising the data, we tried to gave out put to see if is correct or not, and repeat the process again until it work, so we can use it in other situation (Marsland, 2014). This process of our brain was start from the network of neurons, which inspired the computational counterpart as the network of Perceptrons, a model that showing evolutionary capability in learning (Marsland, 2014). The approach of designer using computational power to augment their design capability has always been the central of discussion in the last few decades (see for example Alexander, 1957; Frazer, 1995).The role of the computer, as an a army of ‘rulebook-equipped stupid clerks’ (Alexander, 1967), ‘slave of infinite power and patience’ (Frazer , 1995) or as an ‘idiot savant’ called by Oosterhuis (2003) should be seen as a friendly, open extensions of the designer brain (exo-brains), and can be access however and whenever the designer want . Come to the Modern Digital Age, Mario Carpo diagnosis that “today a new generation of digital craftsmen are increasingly perceiving CAD-CAM technologies as an extension of the mind and hands of the designer, and many of them have embraced traditional, phenomenological and esoteric interpretations of craftsmanship” (Carpo, 2013). This can be link back to the study on perception and embodied cognition of Merleau-Ponty (1964). Furthermore, Pallasmaa (2009) bring the idea of perception on architectural design process, emphasizing the utilization of the tools as an extension of human body and mind. In “The Thinking Hand”, he emphasized that “learning a skill is not primarily founded on verbal teaching but rather on the transference of the skill from the muscles of the teacher directly to the muscles of the apprentice through the act sensory perception and bodily mimesis. This capacity of mimetic learning is currently attributed to human mirror neurons”. With Pallasmaa, “architectural ideas arise ‘biologically’ from unconceptualised and lived existential knowledge rather than from mere analyses and intellect. Architectural problems are, indeed, far too complex and deeply existential to be dealt with in a solely conceptualised and rational manner”. He concluded that architect should “thinking in term of tools, the realization of mechanical connections and the invention of mechanical means for mechanical ends” (Pallasmaa, 2009). This notion of embodied wisdom in our body is reinforcing the hypothesis that consider the computer as and exo-brain, the embodied cognition can only be achieved with the robot as the exo-body for the designer, to develop the learning capability of the machine on the creative and intelligent of design process. However, the fundamental difference of human brain and the computer, and human body with robot should be celebrated in order to utilize the computation power and robotic technology in design (Carpo, 2017). On the political and philosophical consequence of computation and automation, architect and robotic autodidact Michael Sliver stated that : “As innovative hardware and code begin to take over jobs traditionally performed by humans, difficult questions concerning the limits of computation, the value of labor and the nature of intelligence inevitably arise. This runs thinking directly into the philosophical impasse of the mind-body problem. No one interested in the future of buildings and automation can avoid it for long.” (Silver, 2018)

Figure 9: SEEK - the Architecture Machine Group. One of the most successful cybernetic experiment in architecture. (Image : Meetup, 2018) Figure 10: The Universal Constructor, model of a interactive self-organizing system. By Gordon Pask, Julia Frazer and Architecture Association Diploma Unit 11, 1990. ( Image: Frazer, 1995)

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2.3 Cybernetic Even-though the applications of Machine Learning in architecture are relatively new, the idea of learning with a close feedback loop in the design process have been explored since the 70s by pioneer in Cybernetic Architecture (see Pask, 1969; Frazer,1995) . Defined by mathematician Norbert Wiener in 1948, cybernetic is a science of “control and communication in the animal and machine”. Wiener’s thesis that “all animals are machines subject to feedback”, and “the fact that they learn from feedback make them intelligent” (Wiener, 1948). This concept was highly influence the way machine learning process with data and feedback, to be smarter through the loop of training and feedback. The creation of ‘second-order’ cybernetics shifted from the focus on Wiener’s ‘communication and control’ toward ideas of interaction, and included the observer in any system. Architect and cyberneticist Gordon Pask was highly influential in the realm of second-order cybernetics (Spiller, 2002). In the article ‘The Architectural Relevance of Cybernetics’ in Architectural Design 1969, Pask emphasized that the demand for system-oriented thinking for architects was keep raising throughout time opposing with the expectation of only design building. He further argued that pure architecture with the descriptive and prescriptive nature did little to predicts or explain, meanwhile cybernetic theory has an “appreciable predictive power” (Pask, 1969). On the same topic, architect Kas Ooterhuis further argued that building, like other living organism or industrial product, are also input-output machines, which a human perception of the building are totally differently before and after enter it. He suggested that the designer of this age should be conscious of the fact that “all architectural place are basically transaction spaces”, and should embrace the entire flux through the building body in the design process. (Ooterhuis, 2013). Two of the most notable movement of Cybernetic in Architecture are Architectural Machine Group in the 80s from MIT with their influential SEEK project, and The Universal Constructor by Gordon Pask, Julia Frazer and their student at AA Diploma Unit 11, 1990. In “SEEK”, the gerbils was interact with the machine in a continuous feedback loop .By replacing and reacting to these alterations, the gerbil‘s mental understanding of the space was monitoring and adjusting according to their need (Cross, 1977). And with the Universal Constructor, the machine are self-organized and self-evolved, interact with user in different configuration an communicate by visual signal (Frazer, 1995). However, in ‘The Second Digital Turn’, architectural historian and critic Mario Carpo argued that the Cybernetic experiments of the 1960s and 1970s “did not change architecture at all”. He diagnosis that although cybernetic architects have been develop cutting edge technological innovation and proven the positive possibility of digital tools, lack of pragmatic application have not bring this movement out of academic and research context (Carpo, 2017). Within this design thesis, the aim of the architect is to provide a set of constraints that allow for certain modes of evolution. In this case, machine learning with the primary task as predicting output base on large given input data is a logical instrument of applying this theories to real design problems. Nevertheless, the exploration of a fully responsive system with every single cell are robot make cybernetic architecture are too “high tech” and technically redundant for typical building typology. This thesis will only focus on the design and robotic assembly process of a house, with the structure component are passive tiles and constructed by one type of active robot . By doing that, the research will be more practically relevant to the discourse of automation in architecture and construction.

03 20

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MACHINE LEARNING IN DESIGN PROCESS

Having the background of Machine Learning in architecture built up, the second chapter will look at the application of ML in the design process of the autonomous habitat. Not only as a mean to analyse and categorizing the design options, ML also enhance the capability for form exploration and space making. The discretization of the design is raised from the nature of the digital computing process which result in voxel-based architecture. Since the design will be constructed by multiple relative robot, a data-driven approach in the aggregation of the house need to be embraced to the full extend, in order to formulate the assembly process in a digital discrete manner.

Figure 11: Visions of the Future by Villemard in 1910, depicting a construction site in the year 2000. This portrays the architect still in command of the design and construction process with a ‘Plan’ drawing on the desk and a finger on a button to control the robot. The Robot here can be seen as an exo-body for the architect to execute his creative idea in reality. (Image: The Society Pages, 2011). Figure 12: The Endless wall, Gramazio Kohler Research, ETH Zurich. The project developes “cognitive” construction technics with the main objective to apply robotic-based fabrication methods on construction sites and buildings (Image: Gramazio Kohler Research, 2011)

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3.1 Rethinking the Design Process “Just as coping with the mechanical way of making was the challenge of industrial design in the 20th century, coping with the computer’s way of thinking is going to be the challenge of post-industrial design in the 21st century, because today’s thinking machines defy and contradict the organic logic of the human mind, just as the mechanical machines of the industrial revolution defied and contradicted the organic logic of the human body”. (Carpo, 2018) At the moment, architects often use computer to finding solution for rational and logical problems (Medium, 2018). The design process used to be idealistically way of how designer’s intuition and ideology translated to drawing using CAD, then structuralized and functionalized by different specialized consultant, recently prefer through a central Building Information Model (BIM). But with the emerging power of machine learning, computers and robots are evolving to be able to partly perform human creative task (Medium, 2018). The relation between human - machine in the design process therefore should be re-evaluated. In “The Automated Architect”, Cross (1977) has forecast the future of “the machine as architect”, which “giving the machine facilities, skills and independence until it can be regards as a virtual architect in its own right”, even when in his time the machine was still “brutish and stubbornly moronic”. Frazer (1995) argued that with the increasing computational power, the role of the architect is “enhance rather than diminished”, and more design options with higher sophistication level could be generated. Today, significant development in computational logic and computational graphic has basically addressed all the proliferation of ‘bad digital architecture’ norm. The computer now is gaining more and more independence in the creative task, and able to identify and solve problem that excess human perception (Medium, 2018). But not only the machine , designer also need to evolved to adapt to this new type of human- machine relation. Architect Oosterhuis recommended that “well-trained intuition is an absolute requisite for a successful communicative relationship between (wo)men and machine” (2003). He further asserted that the act of learning how to design is fundamentally an act of training the intuition, to making good design is like making good ‘split second ‘decision, and this sensitivity can be and must be trained. Artificial Intuition is like spontaneous action lie between intuition and logic, and connect human intuition to the calculation speed of computer (Oosterhuis, 2003). After all, the initial seed of every single design will always come from human creativity. In the design project, we explore further on the new typology of the digital architecture complied by Carpo on “The Second Digital Turn” : the architect is not only design the building but are the designer of the system (2017). Base on that idea, we develop a central BIM platform integrating generative design and robotic assembly, which is not only assist the architect in architectural creation process, but also able to suggest optimal solution base on training from the user data input and the behaviour of the robot. This new digital architecture typology will be not only just an top-down translation of designer idea, but a collaborative bottom-up process of exploration and conversation between human-machine (see for example Pask, 1969; Frazer, 1995; Carpo, 2017).

Figure 13: EZCT Architecture & Design Research, Study on Optimisation: Computational Chair Design using Genetic Algorithm (Image: Morel, 2004)

Figure 14: Autodeskâ&#x20AC;&#x2122;s Project Dream Catcher, Generative Design of a chair using GA and Machine Learning (Image: Autodesk, 2016)

Figure 15: Topology Optimization of a small shelter using BESO technique.

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3.2 Evolutionary Structure Generation “Could one set in motion a process of creation which required massive feedback but only one seed - Darwinian evolutionary theory, after all, implies one common root for all species. Perhaps we do not even need one seed? Could we evolve architectural life from nothing, with no preconceptions, with no design, just blind tactics? Natural selection has superb tactics, but no strategy - but tactics, if pursued without thought for the cost and for long enough, can get to places which no strategist would dream of” (Frazer, 1995). The experiment of genetic algorithm in the last decades on architecture has proven the ability of generating an evolutionary architecture with evolved from large amount of iterations. Architectural critic Neil Spiller has complied the idea that “good fit” building is comparatively look similar to their generational ancestors, thus “becoming the product of a formal genetic algorithm that determines a search for a ‘fitness’ of some sort, either aesthetic or functional” (Spiller, 1998). Evolutionary biologist Richard Dawkins, in “The Blind Watch Maker”, demonstrated visually that “while random selection and aimless wandering would never produce a coherent design, cumulative section could” (Dawkins, 1986). Using Genetic Algorithm as the training dataset, the machines would learn from previous iterations to create more effective designs. However, the capability of suggesting a reasonable solution from those experiment is still limited. With the recent development in machine learning, this intuitive process is made possible for the designer to reach. The project Dreamcatcher in 2016 form Autodesk, as a comparison to the work of Phillipe Morel in 2004, is showing a more logical approach using machine learning to overcome the limitation of Genetic Algorithm. With Morel’s chair, iteration as a result of random generations from Genetic Algorithm with the final results was selection of the designer which ‘look’ possible for production (Morel, 2004). With Autodesk Dreamcatcher’s chair, after generating thousands of iterations for the chair, a categorization of the option with an optimization process in the selection direction was done by Machine Learning algorithm, to suggest the optimum result for the designer. (Autodesk, 2016) In our design project, we were experimenting with topology optimization using BESO (Bidirectional Evolutionary Structural Optimization) method. By defining the load of the structure and fix support point, a generative process was executed using GA, and through 200 iterations, and optimized structure was generated base on the appropriation for local tensor value of each voxel. This generative method is require minimum input from designer, and the form finding process is leave to be done by the algorithms. The data then is either translated directly to voxelized structure which replace by combination of tile follow a logical movement of the robot, or use as a guidance for the design decision of designer in making the main structure of the house. Figure 16: Topology Optimization of the house, with Stress Field displayed as vector and as colour on voxels

Figure 17: Iterations of the Machine Learning Algorithm on the design of a brick wall (Image: Harrison, 2016)

Figure 18: T-SNE Dimensionality Reduction Technique - Convert Dataset from high dimension space to 2D surface while keeping relation between data. ( Image : Maaten & Hinton, 2008) Figure 19: 1000 iterations of the shelter colour coded follow data categorization result of the unsupervised learning.

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3.2 Data Categorization “Just as we could not easily deal with a random list of a million names when we look for one in particular, we could not easily work with a random heap of 1 million different bricks when we need them to build a house. But that what computers are good at“ (Carpo, 2018). Commenting on the research on human brain of Igor Aleksander, Frazer (1995) point out that the holistic capability of human brain on making guess based on experience, retrieving memories, perceiving analogies and forming associations between unrelated items are aspect of intuition, perception and imagination which are the fundamental fuel for creative architectural ideas. At the moment, those neurological concepts are accelerating the recent development of deep learning artificial neural network, which capable of intuitively making decision based on previous training, clustering unrelated data, and predicting output from large input dataset (DeepMind, 2018). The project from Paul Harrison (2016) which use machine learning to generate multiple iterations, and use physical engine as criteria to elaborate each structure, and apply the rule for next generation. Using the “centuries-long process of trial-and-error structural optimization” as a model reference, the machine learning techniques described in the paper aim to achieved a similar objective. Successful iterations are mutated and replicated until a more optimal generation are established. The ML algorithm, by automating the collapsing process and analysis, converted the traditional brutal approach to a logical computing method for masonry structural optimization (Harrison, 2016). With the application on the design project, the Unsupervised Learning technique was applied to clustering the iterations of a simple emergency shelter design based on different descriptors of the structure. The data was generated by a generative design process, to produce 1000 different iterations as the training dataset for the algorithm. Then, using T-SNE dimensionality reduction technique, the options are clustered and categorized depend on the descriptors and parameters entwined together. In most of the trials, due to the discrete arrangement of the aggregation the data is generally seeming to be clearly dispatch between clusters. By testing with the parameters in simple case, it is possible to develop an intuition for what is going on. The clustering trend for the tile are actually showing useful information for the aggregation of the structure later on. And in most of the case, lower Perplexity from 20-50 was actually work better in term of clustering the data and create visible pattern, when lower Perplexity often scattered the data, which are suggested by Maaten & Hinton(2008). After training, any new given design could be clustered into those cluster base on the parameter that it has. The result is generated not just only through an optimization process, but also an intuitive appropriation of the machine to select the most appropriate design option which balance all the condition. By utilizing Machine Learning Algorithm, hidden pattern was revealed , suggest a optimal structure for designer. Using “artificial intuition”, this algorithm help dealing with large amount of design iterations as data that difficult for human to perceive but are a relatively simple computation task. This is partly contributing to the idea of using computational power as an external brain which help design better.

Figure 20: Project Discover: An application of generative design for architectural space planning (Image: Danil et al, 2018) Figure 21: Fundamental Activities of human living inside the house was the input to determined the grown direction of the structure.

POINT OF ACTIVITIES

AREA AND CONNECTION

MASS AND VOID

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3.3 Spatial Arrangements Optimization Since the 70s, the spatial-arrangements, or “room layout” problems, was often utilized as the basis for comparisons of human and machine performance in solving design problems (Cross, 1977). In order to design an inhabitable habitat, multiple unpredictable data like user preference and environmental affection should be digitalized, translate to usable data for the machine learning algorithm. The case study for this design task are from Autodesk Research Lab (2017) with the “Project Discover” which are an application of generative design for architectural space planning. The goal of the projects was to create a vibrant and effective working space in Toronto. Setting up six unique objective that evaluate each layout based on architecture performance as well as worker- specific preferences , the project aim to generate comprehensive dataset to feed the evolutionary design method in the search for the best solution which balance all aspect. Using Multi-objective genetic algorithm (MOGA) as the generator to scan through the high-dimensional space of possible solutions, then describe several visualization tools that can help a designer to navigate through this design space and choose good design. The advantage of this method compare with other GA algorithm is that it can take large number of parametric geometric function and human input data which is not be easily differentiated, and only can be optimized through stochastic experimental process. (Danil et al, 2018). After 10,000 design options, a semi-intelligent leaning model was applied on the data analysis to suggest the optimal solutions. Through inheritance, input space, metric space analysis and clustering, statistically dominant design options were highlighted for further manual analysis by designer to develop into a final design (Danil et al, 2018). The project has tackled the complexity of dataset in the generation of the space, however the design solution quality as based on continuity principle seam to not very efficient in the computing process. In our design proposal, a discrete process will be applied to optimize the speed of generate and analyse the solution. We aim to create a fully functional and inhabitable house. By taking the fundamental acts of human living inside the house, and shelter it with the combinatorial logic derived from robot movement, the spatial organization of the house could be digitalized, giving data for further machine learning algorithm. We develop an BIM application which allow user to defined the point of activity inside the house with some basic properties like privacy level of the space. Then the application will automatically generate floor slab, connection, mass and void for inhabitable space. We aim to further develop a machine learning algorithm for this arrangement, taking more input data from user like daylight, view to outside, adjacency preference and live style to computed a responsive spatial quality. “Because a computer can process information much quicker than a human, such a system allows a much deeper exploration of complex design spaces. With a model of sufficient complexity, however, a generative design with machine learning base system can also be used to reveal interesting parts of the design space and discover novel design solution that would otherwise be hidden to the human designer. In short, computer is augmenting designer intuition and creativity” (Danil et al, 2018).

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MACHINE LEARNING IN AUTONOMOUS ASSEMBLY

This final chapter will examine the ML algorithms for the autonomous assemble sequence of the On-site Robot. Because the robot and the building component are geometrically identical, they become different variable of the same object-oriented algorithm, integrated to the full extend. In order to achieve the full automation task, broad cross-disciplinary integration is required: architecture, structure , robot hardware and software; all be developed together under a BIM platform. The assembly logic of the robot is the core for the aggregation algorithms. With the complexity of design which keep growing along with the scale, it is essential to apply machine learning algorithm to simulate the robot locomotion for any given structure. Multiple robots operating at the same time will required a hybrid of Central -Local control system, to maximize efficiency and collaboration between robots.

Figure 22: Diagram showing ‘Digital and Distributed’ as the fourth industrial revolution by WikiHouse (Image: Wiki House Foundation, 2015)

Figure 23: Facit Homes’ on-site factory of a flat-bed CNC machine in a shipping container. (Image: Facit Homes, 2017) Figure 24: System of manufacture, logistic and assembly sequence for Assembler Assemble

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4.1 Rethinking the factory: “At this point in the evolution of autonomous systems, we are beginning to see a shift away from heavy, fixed-in-place manipulator to highly mobile devices capable of working alongside human in dynamic environments. By upgrading the space of a typical daylight factory, the following project seeks sustainable ways of making architecture and the construction process respond to the demands of an ever-evolving digital economy. “(Silver, 2018). Machine learning is not only transform the way we design, but also the way we making building. Toward the Fourth Industrial Revolution with emerging technology breakthrough in artificial intelligent and robotic, the definition of the traditional factory and the way we manufacture is shifting to leaner, smarter and more flexible forms of production. (Schwab, 2016). And machine learning algorithm with it capability to adapt and evolve will be an inevitable factor for this new mode of production. The shift from mass-standardization toward mass-customization also redirect the preference from centralized production toward decentralized models (see Schwab, 2016; Carpo, 2017) . A distributed production chain which integrated design and manufacture is proving as a smarter process, enable the optimisation of production. In additional, the use of machine learning for data collection and analytic will build up a more responsive manufacturing (Arup Foresight, 2018). Distributed production and real-time making is “not just about understanding the design intent inherent in the digital representations, but also about sensing and on the fly responding to the contingencies of the realities of making”. The supported of AI could bring designing and making adjoined in to a real-time activity. (Andrew & Mahes, 2018). This distributed model create a new definition for on site factory which coined as “sky factories”, with the basic principle is to create a structured working environment, where building components are linked with the final product, covering the working area and continuously constructed while manufactured (Bock & Linner, 2015). This concept are contradicted to the traditional model that “factory is turned inside out: the object being created is itself the framework for the factory” (Carney, 2015) WikiHouse and Facit Home are two representative examples for the idea of distributed manufacturing. WikiHouse was an open source construction system, with the aim to create a common platform which possible for anyone to downloads designs and build affordable but high quality houses fit to their demands without construction machineries and skills (WikiHouse, 2015). Facit Homes, using an on-site CNC cutter packed in a container, has been manufacturing house with an “just in time” schedule, and adjusting the any minor error of the component design on the construction process (Facit Homes, 2017). However, both of those fabrication models due to the irregularity of the component make it difficult for automated the construction process with robot. With the design project, we aim to develop a whole automated system of production from manufacturing, logistic and robotic assembly sequence. Through a common syntax between the robot and building component, the production chain could be wholly digitalized and integrated in one central BIM Platform. Data of the of this digitalized process could further harvest the power of machine learning, turning the system become an essential part of the new digital economy, with each contributor interact with the same data in different ways.

Figure 25: Evolved Virtual Creatures, by Karl Sims, 1994. Creature evolved for walking, jumping, swimming, competing for the possession of a cube within this simulated world. The winner of each round of the competition receives a higher score, giving it the ability to survive and reproduce. (Image: Karl Sims, 1994)

Figure 26: Neural Network diagram for the robotâ&#x20AC;&#x2122;s locomotion

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Output

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4.2 Locomotion Neural Network In 1974, J.Rose express in ‘The Cybernetic Revolution’ that in order to effectively transit into the Age of Automation, man had to find a way of designing machines which could be made independent of human control, even when performing tasks of great complexity that are beyond his physical abilities to duplicate. With automation, one is concerned with a machine which controls itself throughout long sequences of tasks without human intervention, the whole process being geared to predetermined requirements, which may or may not have been extrinsically set by the human operator. (Rose, 1974) Kart Sims, with his research on the “Evolving Virtual Creatures” (1994), achieved a breakthrough in the study of Artificial Life (a-life), which demonstrated the ability of Machine Learning on the simulation of creature’s evolution to perform difference task like swimming, walking, jumping, following, and even competing to gain control of a cube. After each generation, based on the fitness of how well the performance was, some creature were survived and reproduce (Sims, 1994). The strategic approach of this project using primitive geometry and limited joints, through internal neural nodes to create the essence of living, self-motivated animals are the aspiration for the development of our robot’s locomotion system. The aim of the machine learning application for the robot in this design research is to use reinforcement learning to train the robot as the agent to pick a tile and place it in a predefined location on the structure, then go back to the picking point and restart the loop. The simple input-output for the task are the picking and placing position, but due to the complexity movement of the robot, it is difficult to figure out all the step of the robot in the process to build the structure. Using Unity ML-Agents beta 2.0 asset, we trained the robot through Tensorflow framework to complete several tasks in an interactive environment. Unity ML-Agents uses a reinforcement learning technique called Proximal Policy Optimization (PPO). PPO is a “neural network to approximate the ideal function that maps an agent’s observations to the best action an agent can take in a given state” (Unity, 2018). The agent learns to behave in environment depending on the rewards and penalties on every steps of training. The information that need to feed to the algorithm including states, actions and rewards for each single generation of training. Based on the global behaviour of each agents in the previous step, the neural network of the robot use regression technique to predict a sequence of actions, steering toward the desired location. In the feed-forward process, each of the feature above will have a weight at the beginning base on provided dataset, and fire off perception through several hidden layers to figure out the step that the robot need to take. When the robot goes on the wrong directions, a back-propagation process will be executed, to readjust the weight of the action at the state that the movement go wrong. This process will gradually create a trained network, which will be able to predict the correct sequence of movements when a new placing position appeared. The instruction given for the robot are for the training process are minimum and neutral. States including random picking and placing position and the relative position of the robot on each step. Actions is adding local torque on 4 different direction. The reward method are if the robot reach the target, it get 1 point, and if it moving far away from the target the reward will be decrease by 0.005. To increase the speed of training, 10 agents was used on the same environment. Before training, the robot are not even able to do one

Figure 27: Sequence of training the robot using Reinforcement Learning in Unity Machine Learning Agents environment.

Figure 28: Sequence of training for robot on uneven landscape.

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single flip. At the start of training, the robot was flipping with random direction and force. After 10000 steps, with the mean reward of 0.5387, the robot start to move smoothly to the target. Then, obstacle are introduced to add challenges for the training process. This “holistic training “method was recommended by Unity Developer, to let the robot be able to archive incremental achievements. At 20000 steps, the robot are still not able to climb over the obstacle. But a breakthrough happen at step 30000, with 3 mutated agents start to learn to add more torque to climb over the obstacle. And at step 50000, with mean reward at 0.9432 all of the agents are able to climb over the obstacle to get to placing point more efficiently and confidently. The next experiment was to train the robot on uneven environment. Using the same training data, 6 agents were already able to climb over a sloppy landscape. By adding the local state of the landscape and after 30000 more steps of training, all the robot are able to adjust torque locally to flipped over bumpy landscape to reach the target. This training give the robot ability to not just only working on a controlled factory, but also on unexpected built environment. On the practical application, machine learning algorithm is suitable for the robot to build the foundation on irregular landscape, create an even base slab, then typical grid-based algorithm are used to build the main structure. The physical hardware prototype of robot would need an digital gyroscope , or an Internal Measurement Units to report position and rotation state of the robot, feeding the real-time machine learning algorithms could be apply to re-correct the robot when it goes off grid. The behaviour of robot here is not following hard-coded instructions, but a intuitive decision making behaviour which react to unfamiliar situation through learning from experience. And those behaviour of the robot become the fundamental instrument of the design process, creating structure and space which are excessing designer intention and imagination. Therefore, the robot here could possibly evolved to be an external body for designer, with its own autonomy to exploring new typology of space which are the instrument to express of “a new form of intelligence”.

Figure 29: Strategy to build on uneven landscape: use trained robot to walk and adjust position to set the foundation, adjust the base height, then build raised platform for grid-base assembly.

Figure 30: Typical sequence for the â&#x20AC;&#x153;forceawareâ&#x20AC;? robot build on variant in an unstructured terrain. The material distribution causes the structure to tilt, exposing newly viable positions to install struts (Image: Melenbrink et al. (2017))

Figure 31: The OSCR (On-Site Construction Robot) 4 by Michael Silver - this robot carries bricks in order to assist human workers. (Image: Silver, 2015)

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4.3 Real time decision making algorithms Come to real on-site construction, due to the irregular and unpredictable environments require a fully flexible and adaptive autonomous control system for the robot. Most of the recent research in real-time decision-making algorithms using a combination of simulation and physical sensors to compensates for minor mistakes which posing possibility to accumulated into more severe structural failures (Petersen & Nagpal, 2014). To archive a fully automated robotic construction processes, coordination through real-time autonomous behavioural rules that instruct robotic building actions is a crucial requirement. Melenbrink et al (2017) develop an on-site climbing robot on a strut system which capable of force-sensing, giving feedback about the structural status of local connection to inform the global structural behaviour. Base from that structure behaviour, the grow direction is updated in real-time, responding to unexpected situation happen on the site. Simple sensor was installed on the robot to detect the torque force in the local connection, compute through a simulation to determine the robot action locally. Robot was communicate indirectly to avoid challenges of ad-hoc wireless network in isolated and chaotic environments (Melenbrink et al, 2017). The machine learning algorithm propose by Silver for the On-site Construction Robot (OSCR 4) make combined use of machine learning to evolve new and more responsive system behaviours over time. Additional algorithms for natural language that assist real-time command and control, using wireless networks for coordinating both human-to-machine and machine-to-machine communication. ML could also be used for semantics-based computer vision to identify the objects properties, and autonomous navigation through unexpected working environment. Through training, the robot mobility, speed and energy efficiency will be improved. (Sliver,2018) However, the control system proposed for this robot is over complicated for the simple task of carry couple of bricks up and down. By integrated the robot in the design process, the application of Machine Learning on the robot control will tackle more fundamental role on the aggregation of the structure. To synthesise the computational method of our design project, we further develop the BIM application with 2 building mode. The first mode is manual build, which give the user ability to interactively aggregate the desired structure, potentially in Virtual reality environment. The building sequence is incorporate with structural analysis, giving the user interactive decision on the grow direction of the structure. The aggregation and assembly process are also based on both structure simulation and force-sensing system on the relative robot. Using Unity and NDVIA PhysX 3.3 engine, a real-time structure analysis was constantly running in the process of building a simple column â&#x20AC;&#x201C; beam structure. The grow direction was govern by the simulation of structural behaviour. Using counterbalancing strategy, the robot could be able to build up the structure without the need for scaffolding, with attention to local force distribution through every sequence of aggregation. When the robot detect a broken point in the structure, it can send signal to call other robot to help fixing the structure. Physical hardware prototype will integrate an Force Resistance Sensor (FRS) to the end-effector to report about the local force and torque. Difference iteration of this process will served as dataset for machine learning algorithm in future development. Through training with real-time sensory data, the robotâ&#x20AC;&#x2122;s capability to tackle with unexpected environment will increase overtime, giving it more freedom and autonomy in local decision making while building the structure.

Figure 32: The sequence that the robot assemble a boxâ&#x20AC;&#x201C;beam like structure using vertical & counterbalance building strategy.

Figure 34: Sequence of robot fixing the structure when detect local broken point or global misalignment.

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Figure 33: Assembly sequence for a section of the house, which base on robot behaviour movement, and build in counter-balancing strategy.

Figure 35: Manual build mode of the BIM application, which integrated a real-time structure analysis to determined the grow direction of the structure.

Figure 36: TERMES robots build a grid-base structure from passive building blocks. A 'structpath' created by a Complier to inform the robots of the sequence of building structure (Image: Werfel, et al., 2014) Figure 37: HyperCell (an AADRL project) uses a flexible structure, internal actuators and magnets to move and self-assemble. (Image: Arch20, 2015)

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4.4 Collaborative Autonomous Assembly With multiple robots working collaboratively to build a structure, a strategic approach need to be consider to maximize efficiency and avoiding collision. Inspired by nature, the basic behaviour of swarms robotic was study and applied in the projects to create a comprehensive global control system arrived from local interaction between robot and robot, and between robot and the built structure. The 3 case studies bellow is approaching this problems in different perspective, but all follow a common logic of collaborative robotic which inspired by swarm behaviour in nature. TERMES (Termite-Inspired-Robot Construction Team) from MIT in 2011, uses biologically inspired robots to collaboratively construct structures from passive building components without the need to store the large data of the overall structure on each robot (Werfel, et al., 2014). TERMES are given a ‘structpath’ or ‘movement guidelines’ to build a particular structure (Werfel, et al., 2014). Hence, all communication between the robots is ‘implicit via the joint manipulation of a shared environment’ (Werfel, et al., 2014). The robots each have in mind what they are trying to build, so individually can effect changes to the structure to align it more closely with the target. The objective of the TERMES project was to propel the point of building complex frameworks that accomplish particular human-outlined objectives, and exhibited that physical equipment can permit the discretized hypothesis to adequately speak to the continuous reality (Werfel, et al., 2014). Nevertheless, the “insect” shape of the TERMES robot limit itself from being a actual construction robot for humans structure, thus not able to climb vertically or work collaboratively in local scale with each other. Therefore, by simplify the shape of the robot to be identical with the building component, they become common syntax for a fully collaborative system on a grid-base frame work. The work of Theodore Spyropoulos and his students at the AADRL ( Architectural Association Design Research Lab) has been exploring with active robots which are also building components. By perceiving architecture as an “ecology of interacting agents” and exploring autonomous self-assembled and self-aware systems, dynamic spatial reconfiguration are facilitated using responsiveness and machine learning (Spyropoulos, 2017). HypperCell is one of the notable project of the lab, propose an architectural system that are responsive through self-awareness, mobility, softness and reconfigurability. Each cell are able to aware the surrounding and the local cell population. The system can create space from global data through local decision making. Multiple swarm robot base on a pattern based aggregation with collision checking giving local decision which influence the global behaviour of the structure (Shokir et all, 2015). Alternative approach from Robert Stuart Smith studio in AADRL was researching on the use of drone for on-site construction as adaptive, rapid and on-demand method. The SCL project develop a resin deposition aerial 3d-printing of bridge-like structures. Collision checking are more emphasized on this type of construction as it will cause serious failure of the physical hardware. The software enabled the quad-copter to “listening” to each other, negotiate in space and time with each other. Machine learning are use to give the quad-copters to undertake predictive structure analysis in real-time and adapt their printing behaviour accordingly. Unpredictable event such as wind , changing in structural conditions was negotiated through reinforcement learning algorithms, giving the structure full adaptability (Smith, 2017).

Figure 38: SCL ( an AADRL Project) using quad-copter to aerial 3d-printing of bridge-like structures (Image: Kokkugia, 2016)

Figure 39: Diagrammatic proposal for the Control System of the robots, and the path-finding principal. POINT OF ACTIVITIES

VOXELIZED OPTIMIZ ED STRUCTURE

POINT OF ACTIVITIES

AREA AND CONNECTION

VOXELIZED OPTIMIZ ED STRUCTURE

STRUCT PATH FOR 1 ROBOT

MASS AND VOID

STRUCT PATH OF MULTIPLE ROBOT

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In the assembly sequence of the autonomous habitat, collaboration strategy required at both global and local scale. The local collaboration strategy are when two robot interlocked to climb up and deposited tile. This strategy can help the robot to overcome many limitations of one single robot, but computing precise collaboration between two robot was posing double challenges physically and digitally. The global collaboration strategy is base on a three dimensional grid system, with the main purpose is to distributed the robot evenly to build different section of the structure, and pre-calculate path and sequence to minimize congestion and collision. The aggregation sequence of a small shelter was computed using path-finding algorithms such as A*, which allow the robot to find the shortest route to from a picking tile position to the placing position without colliding with each other. 6 robots collaboratively build up a small box structure with 150 tiles. Majority of the assembly sequence was completed with robot working independently on different section of the shelter. However, with the upper part of the wall and the roof, two robots were required to work in collaboration in order to climb up and placing the tile. Local position and rotation of the robot was stored in a small memory to construct a feedback loop system for training purposed. Finally, we further developed the second mode of the BIM application which aim to automate a complete process of design a house and compute the assembly sequence for multiple robots to build the structure. First step is to compute the climbable surface for the robot. A Finite Element Analysis was run occasionally to allocate the grow direction according to the structural behaviour. Then the path-finding algorithm generate the struct path for multiple robots collaboratively build the structure, and locally decided if the local point is adequately stable to deposit the tile or not. A higher cost was tagged for the path that was used by one robot, so the other robot will priority to find an alternative route to increase the system efficiency. Where two route crossing each other, a â&#x20AC;&#x153;traffic lightâ&#x20AC;? logic is added to avoid collision. The pattern of the joint are actually the trace of the path that robot use to build the structure. At this stage, the complexity of assembly sequence require a global systematic organization of path planing and collision checking. However, the ML algorithm with it capability to evolve and optimize from simple local rule to create a global complexity would give more flexibility and autonomy for the robot on the assembly sequence of the structure. The strategy of designing the building components and the robot to have identical geometry of a flat voxel was not only essential for the digitalization process of the whole project, but also make it easy for computing collaboration between robots on a coordinated system. Further research on swarm behaviour in the nature could enhance the collaboration between robot, to become a fully autonomous system.

Figure 40: Assembly sequence of a small shelter using collaborative aggregation strategy. The shelter can be deployed to disaster area where human labour is insufficient.

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Figure 41: Global collaborative building strategy. Robot are distributed evenly to build different section of the structure, and pre-calculate path and sequence to minimize congestion and collision.

Figure 42: Assembly sequence of the House. Multiple robot working collaboratively to build the structure in a grid-base frame work.

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CONCLUSION

Figure 43: Proposed Neural Network for the BIM application, which digitalized the partto-whole relationship in the design process.

Figure 44: Digitalized input and output of the neural nets.

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Today, machine learning is a game-changer solution which are widely affecting all fields of modern society, architecture included. However, to be able to embrace this powerful tool to architectural design, not only the technical aspect of the computational method need to be addressed, but the fundamental ideology of machine learning and it relation with human also need to be aware of. Carpo commented on the use of machine learning in architecture as “ regardless of any metaphysical implications, no machine-learning system can optimize all parameters of a design process at the same time; that choice is still the designer’s “ (2018). The BIM application that we develop is part of a new type of construction platform, which not just create a common syntax between generative design and robotic assembly, but also able to utilized artificial computational intuition to suggest optimal solution for designer. The new typology of architecture created by this process are the representation for a new form of intelligent in design process, bridging the gap between “alienate architecture” create by computer and a domestic habitable environment for human . The complexity of the design require the creation process of future building as a collaboration between human and machine, together create a sustainable production chain on the turn of The Fourth Industrial Revolution.

Outlook Further development on the application to apply machine learning in a broader sense of the design process. To turn the aggregated structure in to fully functional house, the application should be able to addressing the supplemental aspect of the house like electricity, lighting, heating, pumping in to the aggregation strategy. In additional, this common BIM construction platform could gather data from multiple user, learn form multiple design strategy that human make, and through unsupervised training to give better suggestion for the designer. Experiment with machine learning for physical hardware prototype will further reinforce the exo-brain and exo-body hypothesis. The algorithm could be applied also for the reconfiguration process of the structure base on data input from human occupancy and external environment condition. By doing that, the structure will finally become a living structure, a “machine for living of the 21st century” (Ooterhuis, 2003). “The home would become not only a human habitat but also a habitat for multiple networked intelligences. Once linked together, these individual smart objects will interact to create the meta-intelligence of the home. Such ecologies would live up to the old adage that ‘ the whole is greater than the sum of the parts’. Building and their components could become conscious - alive” (Spiller, 1998).

Figure 45: Visualization of the living space

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Figure 46: Visualization of the sleeping space

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REFERENCE

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LIST OF FIGURES Figure 1a: Gramazio, F. and Kohler, M. (2018). Gramazio Kohler Research. [online] Gramaziokohler.arch.ethz.ch. Available at: http://gramaziokohler.arch.ethz.ch/web/e/ forschung/216.html [Accessed 12 Jul. 2018]. Figure 1b: Werfel, J., Petersen, K. & Nagpal, R. (2014). Designing Collective Behaviour in a Termite-Inspired Robot Construction Team. Science, 14 February, Volume 313, pp. 754758. Figure 3 & 6: DeepMind (2018). [Online] Available at: https://deepmind.com/research/ alphago/ [Accessed 18 April 2018]. Figure 4: Boston Dynamics (2016). [Online] Available at: https://www.bostondynamics. com/atlas [Accessed 03 May 2018]. Figure 5: Bisintek (2011). [Online] Available at: http://bisintek.com/science/2017/12/27/knowing-basic-artificial-intelligence/ [Accessed 03 May 2018]. Figure 7: Machina speculatrix (1950). [Online] Available at: http://www.cerebromente. org.br/n09/historia/documentos_i.htm [Accessed 18 April 2018]. Figure 8 & 31: Silver, M. (2018). Rise of the Servant Zombies. In Even When They Do Nothing, Robots Are Evocative - Towards a Robotic Architecture. Applied Research and Design Publishing, Novato, USA, pp. 28-37. Figure 9: Meetup (2013). [Online] Available at: https://www.meetup.com/silicon-valley-artificial-Intelligence/messages/boards/thread/9766574 [Accessed 20 February 2018] Figure 10: Frazer, J. (1995). Themes VII: An Evolutionary Architecture. London: AA Publications Figure 11: The Society Pages (2011). Villemardâ&#x20AC;&#x2122;s Vision of the Future. [Online] Available at: https://thesocietypages.org/socimages/2011/03/09/villemards-vision-of-the-future/ [Accessed 20 April 2017]. Figure 12: Gramazio, F. and Kohler, M. (2018). Gramazio Kohler Research. [online] Gramaziokohler.arch.ethz.ch. Available at: http://gramaziokohler.arch.ethz.ch/web/e/ forschung/216.html [Accessed 12 Jul. 2018]. Figure 13: Morrel, P. (2006). Computational Intelligence: The Grid as a Post-Human Network. In The Digital Turn in Architecture 1992-2012, London: Wiley, pp.200-207. Figure 14: Autodesk (2016). [Online] Available at : https://autodeskresearch.com/projects/dreamcatcher [Accessed 10 February 2018]. Figure 17: Harrison, P. (2016) What Bricks Want: Machine Learning and Iterative Ruin. Toronto: University of Toronto.

Figure 18: Maaten,L. and Hinton,G. (2008). Visualizing Data using t-SNE. In Journal of Machine Learning Reseach 9. pp2579-2605. Figure 20: Autodesk (2018). Project Discover: An application of generative design for architectural space planning. [Online] Available at: https://autodeskresearch.com/projects/project-discover [Accessed 18 March 2018]. Figure 22: Parvin, A. & Reeve, A. (2016). Scaling the Citizen Sector. [Online] Available at: https://medium.com/@AlastairParvin/scaling-the-citizen-sector20a20dbb7a4c [Accessed 20 April 2017] Figure 23: Facit Homes (2017). Information. [Online] Available at: http://facit-homes. com/made-with-intelligence [Accessed 20 April 2017] Figure 25: Sims, K. (1994). Evolved Virtual Creatures. Cambridge : MIT Press. Figure 30: Melenbrink, N., Kassabian, P., Menges, A. and Werfel, J. (2017). Towards Force-aware Robot Collectives for On-site Construction. ACADIA 2017 | DISCIPLINES + DISRUPTION, pp.382-391. Figure 36 : Werfel, J., Petersen, K. & Nagpal, R. (2014). Designing Collective Behaviour in a Termite-Inspired Robot Construction Team. Science, 14 February, Volume 313, pp. 754-758. Figure 37: Arch20 (2015). HyperCell Thesis - AADRL. [Online] Available at: http://www. arch2o.com/hypercell-thesis-aadrl/ [Accessed 18 March 2018]. Figure 38: Smith, S. (2018). AADRL SWARM PRINTING: AERIAL ROBOTIC BRIDGE CONSTRUCTION. [online] Kokkugia.com. Available at: http://www.kokkugia.com/filter/teaching/AADRL-swarm-printing-aerial-robotic-bridge-construction [Accessed 12 Jun. 2018]. All other images are by Team M4G, Research Cluster 4, MArch Architecture Design 17/18, The Bartlett School of Architecture, UCL