5 citations · 8 across the 6 of their papers we have counts for
8 papers
A-ULMPM: An Arbitrary Updated Lagrangian Material Point Method for Efficient Simulation of Solids and Fluids
Haozhe Su, Tao Xue, Chengguizi Han +1
We present an arbitrary updated Lagrangian Material Point Method (A-ULMPM) to alleviate issues, such as the cell-crossing instability and numerical fracture, that plague state of t…
Spring-Rod System Identification via Differentiable Physics Engine
Kun Wang, Mridul Aanjaneya, Kostas Bekris
We propose a novel differentiable physics engine for system identification of complex spring-rod assemblies. Unlike black-box data-driven methods for learning the evolution of a dy…
Sim2Sim Evaluation of a Novel Data-Efficient Differentiable Physics Engine for Tensegrity Robots
Kun Wang, Mridul Aanjaneya, Kostas Bekris
Learning policies in simulation is promising for reducing human effort when training robot controllers. This is especially true for soft robots that are more adaptive and safe but…
Model Identification and Control of a Low-Cost Wheeled Mobile Robot Using Differentiable Physics
Yanshi Luo, Abdeslam Boularias, Mridul Aanjaneya
We present the design of a low-cost wheeled mobile robot, and an analytical model for predicting its motion under the influence of motor torques and friction forces. Using our prop…
A Novel Approach to Generate Correctly Rounded Math Libraries for New Floating Point Representations
Jay P. Lim, Mridul Aanjaneya, John Gustafson +1
Given the importance of floating-point~(FP) performance in numerous domains, several new variants of FP and its alternatives have been proposed (e.g., Bfloat16, TensorFloat32, and…
A First Principles Approach for Data-Efficient System Identification of Spring-Rod Systems via Differentiable Physics Engines
Kun Wang, Mridul Aanjaneya, Kostas Bekris
We propose a novel differentiable physics engine for system identification of complex spring-rod assemblies. Unlike black-box data-driven methods for learning the evolution of a dy…