28 citations · 50 across the 10 of their papers we have counts for
11 papers · 1 filter
PhysibleMDM: Enforcing Inference-Time Physical Feasibility Constraints on Motion Diffusion Models via Noise Optimization
Maximilian Stölzle, David Müller, Agon Serifi +3
Motion Diffusion Models (MDMs) can generate diverse and visually appealing human motion; yet, the resulting trajectories frequently violate physical constraints, limiting their dow…
From Digital to Physical Reservoir Computing: Co-Optimizing Soft Robotic Reservoirs via Dynamics Matching
Nicola Visentin, Maximilian Stölzle, Mariano Ramírez Montero +3
Soft robotic substrates are promising for Physical Reservoir Computing (PRC) because their compliant nonlinear dynamics can provide temporal memory, high-dimensional state transfor…
SoRoMoX: Fast, Differentiable, and Parallelizable Soft Robot Models
Maximilian Stölzle, Solange Gribonval, Daniel Feliu-Talegon +9
Reduced-order models based on Cosserat-rod theory are now well established, and modeling theory is no longer the primary bottleneck in soft-robot control. Their implementations, ho…
Symbolic Learning of Interpretable Reduced-Order Models for Jumping Quadruped Robots
Gioele Buriani, Jingyue Liu, Maximilian Stölzle +2
Reduced-order models are central to motion planning and control of quadruped robots, yet existing templates are often hand-crafted for a specific locomotion modality. This motivate…
Contact-Aware Safety in Soft Robots Using High-Order Control Barrier and Lyapunov Functions
Kiwan Wong, Maximilian Stölzle, Wei Xiao +3
Robots operating alongside people, particularly in sensitive scenarios such as aiding the elderly with daily tasks or collaborating with workers in manufacturing, must guarantee sa…
Soft yet Effective Robots via Holistic Co-Design
Maximilian Stölzle, Niccolò Pagliarani, Francesco Stella +6
Soft robots promise inherent safety via their material compliance for seamless interactions with humans or delicate environments. Despite progress, the field struggles to balance t…
Learning Low-Dimensional Strain Models of Soft Robots by Looking at the Evolution of Their Shape with Application to Model-Based Control
Ricardo Valadas, Maximilian Stölzle, Jingyue Liu +1
Obtaining dynamic models of continuum soft robots is central to the analysis and control of soft robots, and researchers have devoted much attention to the challenge of proposing b…
Input-to-State Stable Coupled Oscillator Networks for Closed-form Model-based Control in Latent Space
Maximilian Stölzle, Cosimo Della Santina
Even though a variety of methods have been proposed in the literature, efficient and effective latent-space control (i.e., control in a learned low-dimensional space) of physical s…
Guiding Soft Robots with Motor-Imagery Brain Signals and Impedance Control
Maximilian Stölzle, Sonal Santosh Baberwal, Daniela Rus +2
Integrating Brain-Machine Interfaces into non-clinical applications like robot motion control remains difficult - despite remarkable advancements in clinical settings. Specifically…
An Experimental Study of Model-based Control for Planar Handed Shearing Auxetics Robots
Maximilian Stölzle, Daniela Rus, Cosimo Della Santina
Parallel robots based on Handed Shearing Auxetics (HSAs) can implement complex motions using standard electric motors while maintaining the complete softness of the structure, than…
Reconstructing occluded Elevation Information in Terrain Maps with Self-supervised Learning
Maximilian Stölzle, Takahiro Miki, Levin Gerdes +2
Accurate and complete terrain maps enhance the awareness of autonomous robots and enable safe and optimal path planning. Rocks and topography often create occlusions and lead to mi…