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.
DOC: Differentiable Optimal Control for Retargeting Motions onto Legged Robots
Legged robots are designed to perform highly dynamic motions. However, it remains challenging for users to retarget expressive motions onto these complex systems. In this paper, we present a Differentiable Optimal Control (DOC) framework that facilitates the transfer of rich motions from either animals or animations onto these robots.
ADD: Analytically Differentiable Dynamics for Multi-Body Systems with Frictional Contact
We present a differentiable dynamics solver that is able to handle frictional contact for rigid and deformable objects within a unified framework. Through a principled mollification of normal and tangential contact forces, our method circumvents the main difficulties inherent to the non-smooth nature of frictional contact.
RobotSculptor: Artist-Directed Robotic Sculpting of Clay
We present an interactive design system that allows users to create sculpting styles and fabricate clay models using a standard 6-axisrobot arm.
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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