Papers › Graph networks as learnable physics engines for inference and control
Graph networks as learnable physics engines for inference and control
Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, Peter Battaglia
Understanding and interacting with everyday physical scenes requires rich knowledge about the structure of the world, represented either implicitly in a value or policy function, or explicitly in a transition model. Here we introduce a new class of learnable models--based on graph networks--which implement an inductive bias for object- and relation-centric representations of complex, dynamical systems. Our results show that as a forward model, our approach supports accurate predictions from real and simulated data, and surprisingly strong and efficient generalization, across eight distinct physical systems which we varied parametrically and structurally. We also found that our inference model can perform system identification. Our models are also differentiable, and support online planning via gradient-based trajectory optimization, as well as offline policy optimization. Our framework offers new opportunities for harnessing and exploiting rich knowledge about the world, and takes a key step toward building machines with more human-like representations of the world.
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55cdceb4f1fdcd41 · report
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Weather Forecasting | SD | GN-skip | MSE (t+1) | 0.6543 ± 0.1195 | #9 of 10 | Archive leaderboard | report |
| Weather Forecasting | SD | GN-skip | MSE (t+6) | 0.9872 ± 0.2425 | #9 of 10 | Archive leaderboard | report |
| Weather Forecasting | SD | GN-only | MSE (t+1) | 0.7007 ± 0.0848 | #10 of 10 | Archive leaderboard | report |
| Weather Forecasting | SD | GN-only | MSE (t+6) | 1.0422 ± 0.0673 | #10 of 10 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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