76 citations · 164 across the 14 of their papers we have counts for
17 papers
All You Need Is Low Fidelity: Zero-Shot Sim-to-Real of Learned Robotic Fish Control
Liam Maloney, Simon Ramchandani, Mike Y. Michelis +2
Complex tasks for underwater robots remain limited by the capabilities of their controllers. Learning a better one for a soft, underactuated robotic fish trades simulator cost agai…
Orientation Control of Soft Robots via Adiabatic Spectral Submanifolds
Aron Karakai, Roshan S. Kaundinya, Mike Yan Michelis +2
Soft robots are commonly sought for safety-critical interactions in delicate environments, where accurate position and orientation control is imperative. Model predictive control (…
Swim2Real: VLM-Guided System Identification for Sim-to-Real Transfer
Kevin Qiu, Kyle Walker, Mike Y. Michelis +2
We present Swim2Real, a pipeline that calibrates a 16-parameter robotic fish simulator from swimming videos using vision-language model (VLM) feedback, requiring no hand-designed s…
A Unified Low-Dimensional Design Embedding for Joint Optimization of Shape, Material, and Actuation in Soft Robots
Vittorio Candiello, Manuel Mekkattu, Mike Y. Michelis +1
Soft robots achieve functionality through tight coupling among geometry, material composition, and actuation. As a result, effective design optimization requires these three aspect…
Simple Models, Real Swimming: Digital Twins for Tendon-Driven Underwater Robots
Mike Y. Michelis, Nana Obayashi, Josie Hughes +1
Mimicking the graceful motion of swimming animals remains a core challenge in soft robotics due to the complexity of fluid-structure interaction and the difficulty of controlling s…
Learning, Solving and Optimizing PDEs with TensorGalerkin: an efficient high-performance Galerkin assembly algorithm
Shizheng Wen, Mingyuan Chi, Tianwei Yu +5
We present a unified algorithmic framework for the numerical solution, constrained optimization, and physics-informed learning of PDEs with a variational structure. Our framework i…
SORS: A Modular, High-Fidelity Simulator for Soft Robots
Manuel Mekkattu, Mike Y. Michelis, Robert K. Katzschmann
The deployment of complex soft robots in multiphysics environments requires advanced simulation frameworks that not only capture interactions between different types of material, b…
Sim-to-Real of Soft Robots with Learned Residual Physics
Junpeng Gao, Mike Yan Michelis, Andrew Spielberg +1
Accurately modeling soft robots in simulation is computationally expensive and commonly falls short of representing the real world. This well-known discrepancy, known as the sim-to…
Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World
Nico Gürtler, Felix Widmaier, Cansu Sancaktar +21
Experimentation on real robots is demanding in terms of time and costs. For this reason, a large part of the reinforcement learning (RL) community uses simulators to develop and be…
Low Voltage Electrohydraulic Actuators for Untethered Robotics
Stephan-Daniel Gravert, Elia Varini, Amirhossein Kazemipour +4
Rigid robots can be precise in repetitive tasks, but struggle in unstructured environments. Nature's versatility in such environments inspires researchers to develop biomimetic rob…
Beyond Regular Grids: Fourier-Based Neural Operators on Arbitrary Domains
Levi Lingsch, Mike Y. Michelis, Emmanuel de Bezenac +3
The computational efficiency of many neural operators, widely used for learning solutions of PDEs, relies on the fast Fourier transform (FFT) for performing spectral computations.…
Aquarium: A Fully Differentiable Fluid-Structure Interaction Solver for Robotics Applications
Jeong Hun Lee, Mike Y. Michelis, Robert Katzschmann +1
We present Aquarium, a differentiable fluid-structure interaction solver for robotics that offers stable simulation, accurately coupled fluid-robot physics in two dimensions, and f…
Planar Modeling and Sim-to-Real of a Tethered Multimaterial Soft Swimmer Driven by Peano-HASELs
Stephan-Daniel Gravert, Mike Y. Michelis, Simon Rogler +3
Soft robotics has the potential to revolutionize robotic locomotion, in particular, soft robotic swimmers offer a minimally invasive and adaptive solution to explore and preserve o…
Fast Aquatic Swimmer Optimization with Differentiable Projective Dynamics and Neural Network Hydrodynamic Models
Elvis Nava, John Z. Zhang, Mike Y. Michelis +5
Aquatic locomotion is a classic fluid-structure interaction (FSI) problem of interest to biologists and engineers. Solving the fully coupled FSI equations for incompressible Navier…
Physics-constrained Unsupervised Learning of Partial Differential Equations using Meshes
Mike Y. Michelis, Robert K. Katzschmann
Enhancing neural networks with knowledge of physical equations has become an efficient way of solving various physics problems, from fluid flow to electromagnetism. Graph neural ne…
Sim-to-Real for Soft Robots using Differentiable FEM: Recipes for Meshing, Damping, and Actuation
Mathieu Dubied, Mike Michelis, Andrew Spielberg +1
An accurate, physically-based, and differentiable model of soft robots can unlock downstream applications in optimal control. The Finite Element Method (FEM) is an expressive appro…
On Linear Interpolation in the Latent Space of Deep Generative Models
Mike Yan Michelis, Quentin Becker
The underlying geometrical structure of the latent space in deep generative models is in most cases not Euclidean, which may lead to biases when comparing interpolation capabilitie…