Transformer-based Neural Augmentation of Robot Simulation Representations

We propose to augment common simulation representations with a transformer-inspired architecture, by training a network to predict the true state of robot building blocks given their simulation state. Because we augment building blocks, rather than the full simulation state, we make our approach modular which improves generalizability and robustness.

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Designing Robotically-Constructed Metal Frame Structures

We present a computational technique that aids with the design of structurally-sound metal frames, tailored for robotic fabrication using an existing process that integrate automated bar bending, welding, and cutting. Aligning frames with structurally-favorable orientations, and decomposing models into fabricable units, we make the fabrication process scale-invariant, and frames globally align in an aesthetically-pleasing and structurally-informed manner.

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