46 citations · 83 across the 4 of their papers we have counts for
7 papers · 1 filter
DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models
Tsun-Hsuan Wang, Juntian Zheng, Pingchuan Ma +6
Nature evolves creatures with a high complexity of morphological and behavioral intelligence, meanwhile computational methods lag in approaching that diversity and efficacy. Co-opt…
Efficient automatic design of robots
David Matthews, Andrew Spielberg, Daniela Rus +2
Robots are notoriously difficult to design because of complex interdependencies between their physical structure, sensory and motor layouts, and behavior. Despite this, almost ever…
SoftZoo: A Soft Robot Co-design Benchmark For Locomotion In Diverse Environments
Tsun-Hsuan Wang, Pingchuan Ma, Andrew Everett Spielberg +5
While significant research progress has been made in robot learning for control, unique challenges arise when simultaneously co-optimizing morphology. Existing work has typically b…
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…
Multi-Objective Graph Heuristic Search for Terrestrial Robot Design
Jie Xu, Andrew Spielberg, Allan Zhao +2
We present methods for co-designing rigid robots over control and morphology (including discrete topology) over multiple objectives. Previous work has addressed problems in single-…
ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics
Yuanming Hu, Jiancheng Liu, Andrew Spielberg +5
Physical simulators have been widely used in robot planning and control. Among them, differentiable simulators are particularly favored, as they can be incorporated into gradient-b…