mlsanityData to 7 Oct 2026

Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing

arXiv 1808.03246 ↗First posted 9 Aug 2018cs.RO cs.LGcited by 36

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Abstract (arXiv)

An efficient, generalizable physical simulator with universal uncertainty estimates has wide applications in robot state estimation, planning, and control. In this paper, we build such a simulator for two scenarios, planar pushing and ball bouncing, by augmenting an analytical rigid-body simulator with a neural network that learns to model uncertainty as residuals. Combining symbolic, deterministic simulators with learnable, stochastic neural nets provides us with expressiveness, efficiency, and generalizability simultaneously. Our model outperforms both purely analytical and purely learned simulators consistently on real, standard benchmarks. Compared with methods that model uncertainty using Gaussian processes, our model runs much faster, generalizes better to new object shapes, and is able to characterize the complex distribution of object trajectories.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Aug 2018.

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Citing papers per year: 2021: 8; 2022: 2; 2023: 6; 2024: 10; 2025: 5; 2026: 5.

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  1. 2024PhysGen: Rigid-Body Physics-Grounded Image-to-Video Generationcited by 169
  2. 2025ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skillscited by 104
  3. 2024From Imitation to Refinement -- Residual RL for Precise Assemblycited by 80
  4. 2021Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modelingcited by 59
  5. 2022Learning rigid dynamics with face interaction graph networkscited by 43
  6. 2021From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligencecited by 35
  7. 2021Residual Reinforcement Learning from Demonstrationscited by 27
  8. 2021KNODE-MPC: A Knowledge-based Data-driven Predictive Control Framework for Aerial Robotscited by 26
  9. 2025The Reality Gap in Robotics: Challenges, Solutions, and Best Practicescited by 22
  10. 2023Learning 3D Particle-based Simulators from RGB-D Videoscited by 17
  11. 2021SAGCI-System: Towards Sample-Efficient, Generalizable, Compositional, and Incremental Robot Learningcited by 16
  12. 2022Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecificationcited by 11
24 more
  1. 2021Machine-Learning Non-Conservative Dynamics for New-Physics Detectioncited by 10
  2. 2024QuasiSim: Parameterized Quasi-Physical Simulators for Dexterous Manipulations Transfercited by 10
  3. 2024ElastoGen: 4D Generative Elastodynamicscited by 10
  4. 2025Closing the Train-Test Gap in World Models for Gradient-Based Planningcited by 10
  5. 2023ArrayBot: Reinforcement Learning for Generalizable Distributed Manipulation through Touchcited by 9
  6. 2025Neural Robot Dynamicscited by 7
  7. 2024Generalizing Cooperative Eco-driving via Multi-residual Task Learningcited by 5
  8. 2023Residual Physics Learning and System Identification for Sim-to-real Transfer of Policies on Buoyancy Assisted Legged Robotscited by 4
  9. 2023PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systemscited by 4
  10. 2024Precision-Focused Reinforcement Learning Model for Robotic Object Pushingcited by 3
  11. 2021An Efficient Image-to-Image Translation HourGlass-based Architecture for Object Pushing Policy Learningcited by 2
  12. 2024Physics-Informed Model and Hybrid Planning for Efficient Dyna-Style Reinforcement Learningcited by 2
  13. 2026Learning Physics-Guided Residual Dynamics for Deformable Object Simulationcited by 2
  14. 2021Transfer Learning Approaches for Knowledge Discovery in Grid-based Geo-Spatiotemporal Datacited by 1
  15. 2023Zero-shot Sim2Real Adaptation Across Environmentscited by 1
  16. 2023Neural Partial Differential Equations with Functional Convolutioncited by 1
  17. 2024Physics-integrated generative modeling using attentive planar normalizing flow based variational autoencodercited by 1
  18. 2024Cooperative Advisory Residual Policies for Congestion Mitigationcited by 1
  19. 2024Physics Encoded Blocks in Residual Neural Network Architectures for Digital Twin Modelscited by 1
  20. 2026PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamicscited by 1
  21. 2025ActivePusher: Active Learning and Planning with Residual Physics for Nonprehensile Manipulationcited by 0
  22. 2026Sharpness-Aware Hybrid Model Learning for Architecture-Agnostic Parameter Estimationcited by 0
  23. 2026NeuralActuator: Neural Actuation Modeling for Robot Dynamics and External Force Perceptioncited by 0
  24. 2026Beyond Simulation: Retain-and-Repair Neural Operators for Real-World Adaptationcited by 0