During my internship at NVIDIA, I developed an Omniverse Kit Extension to design, simulate MetaVoxels (robots composed of discrete modular voxels with different mechanical properties). I first developed a robot shape grammar and rules to describe how they can be built. This rules are then used to search for the best robot architecture (shape) and control given a task. Isaac sim was used to simulate the robots and Isaac Gym for the multi-GPU reinforcement learning (RL) workflow. GitLab Link: https://www.cba.mit.edu/amiraa/nvidia-omniverse-metavoxels
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Designing materials based on global properties has been a long-standing goal. By using metamaterials, local cellular structures could be designed to control global properties and surpass continuum material properties. In this research. we introduce an inverse design workflow to design discrete mechanical metamaterial, to build macroscale structures that go beyond 3D printing. The fabrication process and its constraints are inherent in the concurrent micro-macro-optimization. We introduce first a top-down approach. Next, we generalize the previous by introducing a bottom-up approach where we use an inverse design workflow to create a universe library of parts. Comparative analysis will be presented to understand the benefit and cost of each approach in addition to fabricated pieces.
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Modular robotic systems built of reconfigurable components offer an efficient and versatile alternative to traditional monolithic robots. However, as modular systems scale up, construction efficiency is compromised due to an increase in travel time and path planning complexity. We introduce a discrete modular material robot system that is capable of serial, recursive (making more robots), and hierarchical (making larger robots) assembly. The component composition is supported by an algorithm to compile the building blocks into swarms and plan the optimal construction path. Our approach challenges the convention that larger constructions need larger machines to build them, and could be applied in areas that today either require substantial capital investments for fixed infrastructure or are altogether unfeasible. Link to Nature Communications Engineering Paper | Link to MIT News Article | Link to BBC Podcast | Link to Lex Fridman Podcast CBA| 2022 | Group of 3
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Interactive Demo 1: Assembler Control Interactive Demo 2: Voxel Simuation Interactive Demo 3: Convolutional Neural Network The Physical Computing Design tool is an integrated design tool for the design, simulation, optimization and fabrication of reconfigurable computing systems. Traditional electronics design workflows follow a sequential linear process that starts with design, then analysis and simulation, and finally system fabrication and testing. Often these stages are executed independently from each other, using different tools, making it hard to translate the feedback from the simulation or testing stages into viable design amendments. This adds considerable inefficiency to an inherently iterative design workflow. As an alternative, I developed an integrated, closed loop DICE design tool where one can design, simulate, optimize and fabricate re-configurable computing systems. This novel integrated design workflow paves the way for the design of discrete integrated circuits but also reconfigurable computing systems that can change and evolve while execution as needed. CBA | Under Development | Individual Omniverse MetaVoxels Designer Graduate, PhD Inverse Design of Discrete Metamaterial Graduate, PhD
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Link to MIT News Article: Assembler robots make large structures from little pieces Link to Paper: Material-Robot System for Assembly of Discrete Cellular Structures Link to Detailed Documentation and Interactive Demo Research on the inverse kinematics, path planning and control of a swarm of relative robots to assemble discrete digital material. CBA | Spring 2019 | Individual Omniverse MetaVoxels Designer Graduate, PhD Inverse Design of Discrete Metamaterial Graduate, PhD
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Link for Online Interactive Demo. This is an interactive visualization to help people understand how different parameters affect the spread of COVID 19. The simulation has 100,000 particles moving either randomly (or only going to central location), and using the GPU to massively parallelized the computation and visualization of the spread of the disease. On the top left, one could change some parameters like the percentage of people infected at the start of the pandemic, the infection probability given you are at close distance with an infected person, radius where the disease can be transmitted as well as how much time it takes until the symptoms appear. One can also change policies like being quarantined (only a small percentage of the population can move around). At the bottom of the screen a SIR (susceptible-infected-removed) model is calculated to see the total infected vs time and see the efficacy of different strategies when dealing with the pandemic. CBA| Spring 2020 | Individual Omniverse MetaVoxels Designer Graduate, PhD Inverse Design of Discrete Metamaterial Graduate, PhD
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Professor: Panagiotis Michalatos Course: MDes Technology Final Thesis Part Published in ACADIA 2017 In an attempt to design shape-morphing multifunctional objects, this thesis uses programmable matter to design self-organizing multi-agent systems capable of morphing from one shape into another. The research looks at various precedents of self-assembly and modular robotics to design and prototype passive agents that could be cheaply mass-produced. Intelligence will be embedded into these agents on a material level, designing different local interactions to perform different global goals. The initial exploratory study looks at various examples from nature like plankton and molecules. Magnetic actuation is chosen as the external actuation force between agents. The research uses simultaneous digital and physical investigations to understand and design the interactions between agents. The project offers a systemic investigation of the effect of shape, interparticle forces, and surface friction on the packing and reconfiguration of granular systems. The ability to change the system state from a gaseous, liquid, then solid state offers new possibilities in the field of material computation, where one can design a “material” and change its properties on demand. Harvard GSD | Spring 2017 | Individual Omniverse MetaVoxels Designer Graduate, PhD Inverse Design of Discrete Metamaterial Graduate, PhD
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Professor:Neil Gershenfeld Course: How to Make Almost Anything Course Website click here. This course provided a hands-on introduction to the resources for designing and fabricating smart systems, including CAD/CAM/CAE; NC machining, 3-D printing, injection molding, laser cutting; PCB layout and fabrication; sensors and actuators; analog instrumentation; embedded digital processing; wired and wireless communications. It also put emphasis on learning how to use the tools as well as understand how they work. MIT | Fall 2017 | Individual Omniverse MetaVoxels Designer Graduate, PhD Inverse Design of Discrete Metamaterial Graduate, PhD
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Professor: Volkan Alkanoglu Course: Hybrid Formations – Interdisciplinary Design Team Members: Amira Abdel-Rahman and Gabriel Muñoz Moreno The challenge of the seminar is to design and fabricate a structure that is light, paper-thin but still maintains its structural integrity. The objective was to get inspired by techniques of the aviation and automobile industry to the fabrication of these prototypes. Optimizing the structural performance of the chair was the driving force behind the design. A parametric model of the chair was developed and tested under different load case scenarios and then the design was optimized for ease and automation of fabrication. Manufactured out of extremely lightweight, aluminum panels, the Y-chair is CNC/water-jet cut and the the robotic arm was used to form the aluminum sheets. A custom end tool for the robot was fabricated using english wheel rollers to form the sheets using custom CNC milled wood forms. Harvard GSD | Fall 2017 | Group of 2 Omniverse MetaVoxels Designer Graduate, PhD Inverse Design of Discrete Metamaterial Graduate, PhD
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