gradSim: Differentiable simulation for system identification and visuomotor control
Research output: Contribution to conference › Paper › Research
Documents
- gradsim_differentiable_simulat
Final published version, 3.6 MB, PDF document
In this paper, we tackle the problem of estimating object physical properties such as mass, friction, and elasticity directly from video sequences. Such a system identification problem is fundamentally ill-posed due to the loss of information during image formation. Current best solutions to the problem require precise 3D labels which are labor intensive to gather, and infeasible to create for many systems such as deformable solids or cloth. In this work we present gradSim, a framework that overcomes the dependence on 3D supervision by combining differentiable multiphysics simulation and differentiable rendering to jointly model the evolution of scene dynamics and image formation. This unique combination enables backpropagation from pixels in a video sequence through to the underlying physical attributes that generated them. Furthermore, our unified computation graph across dynamics and rendering engines enables the learning of challenging visuomotor control tasks, without relying on state-based (3D) supervision, while obtaining performance competitive to/better than techniques that require precise 3D labels.
Original language | English |
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Publication date | 2021 |
Number of pages | 25 |
Publication status | Published - 2021 |
Event | 9th International Conference on Learning Representations - ICLR 2021 - Virtual Duration: 3 May 2021 → 7 May 2021 |
Conference
Conference | 9th International Conference on Learning Representations - ICLR 2021 |
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City | Virtual |
Period | 03/05/2021 → 07/05/2021 |
Links
- https://openreview.net/forum?id=c_E8kFWfhp0
Final published version
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