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Real2Sim2Real Transfer for Control of Cable-driven Robots via a Differentiable Physics Engine
This strategy is based on a differentiable physics engine that can be trained given limited data from a real robot.
A Recurrent Differentiable Engine for Modeling Tensegrity Robots Trainable with Low-Frequency Data
A model of NASA's icosahedron SUPERballBot on MuJoCo is used as the ground truth system to collect training data.
Sim2Sim Evaluation of a Novel Data-Efficient Differentiable Physics Engine for Tensegrity Robots
The results indicate that only 0. 25\% of ground truth data are needed to train a policy that works on the ground truth system when the differentiable engine is used for training against training the policy directly on the ground truth system.
Spring-Rod System Identification via Differentiable Physics Engine
We propose a novel differentiable physics engine for system identification of complex spring-rod assemblies.
A First Principles Approach for Data-Efficient System Identification of Spring-Rod Systems via Differentiable Physics Engines
We propose a novel differentiable physics engine for system identification of complex spring-rod assemblies.
