Interactive Differentiable Simulation

Abstract

Intelligent agents need a physical understanding of the world to predict the impact of their actions in the future. While learning-based models of the environment dynamics have contributed to significant improvements in sample efficiency compared to model-free reinforcement learning algorithms, they typically fail to generalize to system states beyond the training data, while often grounding their predictions on non-interpretable latent variables. We introduce Interactive Differentiable Simulation (IDS), a differentiable physics engine, that allows for efficient, accurate inference of physical properties of rigid-body systems. Integrated into deep learning architectures, our model is able to accomplish system identification using visual input, leading to an interpretable model of the world whose parameters have physical meaning. We present experiments showing automatic task-based robot design and parameter estimation for nonlinear dynamical systems by automatically calculating gradients in IDS. When integrated into an adaptive model-predictive control algorithm, our approach exhibits orders of magnitude improvements in sample efficiency over model-free reinforcement learning algorithms on challenging nonlinear control domains.

References in corpus (3)

• Interaction Networks for Learning about Objects, Relations and Physics
• DeepMind Control Suite
• Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids

Cited by in corpus (9)

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• CvxNet: Learnable Convex Decomposition
• A First Principles Approach for Data-Efficient System Identification of Spring-Rod Systems via Differentiable Physics Engines
• Follow the Gradient: Crossing the Reality Gap using Differentiable Physics (RealityGrad)
• Additive manufacturing process design with differentiable simulations
• Spring-Rod System Identification via Differentiable Physics Engine