87 citations · 452 across the 47 of their papers we have counts for
9 papers · 1 filter
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…
Decoupling Torque and Stiffness: A Unified Modeling and Control Framework for Antagonistic Artificial Muscles
Amirhossein Kazemipour, Robert K. Katzschmann
Antagonistic artificial muscles can decouple joint torque and stiffness, but contact transients often degrade this independence. We present a unified real-time framework applicable…
AquaROM: shape optimization pipeline for soft swimmers using parametric reduced order models
Mathieu Dubied, Paolo Tiso, Robert K. Katzschmann
The efficient optimization of actuated soft structures, particularly under complex nonlinear forces, remains a critical challenge in advancing robotics. Simulations of nonlinear st…
Robotic Assistant: Completing Collaborative Tasks with Dexterous Vision-Language-Action Models
Boshi An, Chenyu Yang, Robert Katzschmann
We adapt a pre-trained Vision-Language-Action (VLA) model (Open-VLA) for dexterous human-robot collaboration with minimal language prompting. Our approach adds (i) FiLM conditionin…
Beyond Anthropomorphism: Enhancing Grasping and Eliminating a Degree of Freedom by Fusing the Abduction of Digits Four and Five
Simon Fritsch, Liam Achenbach, Riccardo Bianco +9
This paper presents the SABD hand, a 16-degree-of-freedom (DoF) robotic hand that departs from purely anthropomorphic designs to achieve an expanded grasp envelope, enable manipula…
mimic-one: a Scalable Model Recipe for General Purpose Robot Dexterity
Elvis Nava, Victoriano Montesinos, Erik Bauer +8
We present a diffusion-based model recipe for real-world control of a highly dexterous humanoid robotic hand, designed for sample-efficient learning and smooth fine-motor action in…
Latent Action Diffusion for Cross-Embodiment Manipulation
Erik Bauer, Elvis Nava, Robert K. Katzschmann
End-to-end learning is emerging as a powerful paradigm for robotic manipulation, but its effectiveness is limited by data scarcity and the heterogeneity of action spaces across rob…
MAPLE: Encoding Dexterous Robotic Manipulation Priors Learned From Egocentric Videos
Alexey Gavryushin, Xi Wang, Robert J. S. Malate +5
Large-scale egocentric video datasets capture diverse human activities across a wide range of scenarios, offering rich and detailed insights into how humans interact with objects,…
ORCA: An Open-Source, Reliable, Cost-Effective, Anthropomorphic Robotic Hand for Uninterrupted Dexterous Task Learning
Clemens C. Christoph, Maximilian Eberlein, Filippos Katsimalis +8
General-purpose robots should possess human-like dexterity and agility to perform tasks with the same versatility as us. A human-like form factor further enables the use of vast da…