5 citations · 5 across the 2 of their papers we have counts for
4 papers · 1 filter
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…
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…
Adaptive Tensegrity Locomotion on Rough Terrain via Reinforcement Learning
David Surovik, Kun Wang, Kostas E. Bekris
The dynamical properties of tensegrity robots give them appealing ruggedness and adaptability, but present major challenges with respect to locomotion control. Due to high-dimensio…