mlsanityData to 7 Oct 2026

Jade: A Differentiable Physics Engine for Articulated Rigid Bodies with Intersection-Free Frictional Contact

arXiv 2309.04710 ↗First posted 9 Sep 2023cs.RO cs.AI cs.CVcited by 7

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Abstract (arXiv)

We present Jade, a differentiable physics engine for articulated rigid bodies. Jade models contacts as the Linear Complementarity Problem (LCP). Compared to existing differentiable simulations, Jade offers features including intersection-free collision simulation and stable LCP solutions for multiple frictional contacts. We use continuous collision detection to detect the time of impact and adopt the backtracking strategy to prevent intersection between bodies with complex geometry shapes. We derive the gradient calculation to ensure the whole simulation process is differentiable under the backtracking mechanism. We modify the popular Dantzig algorithm to get valid solutions under multiple frictional contacts. We conduct extensive experiments to demonstrate the effectiveness of our differentiable physics simulation over a variety of contact-rich tasks.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 21 of its 50 references that are in the map. First posted on arXiv 9 Sep 2023.

Builds on

21 of 50 references.

References · 21

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2021Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulationcited by 375
  3. 2019DiffTaichi: Differentiable Programming for Physical Simulationcited by 248
  4. 2020Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solverscited by 201
  5. 2020Learning to Control PDEs with Differentiable Physicscited by 131
  6. 2016A Differentiable Physics Engine for Deep Learning in Roboticscited by 107
  7. 2021gradSim: Differentiable simulation for system identification and visuomotor controlcited by 100
  8. 2018ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Roboticscited by 98
  9. 2020NeuralSim: Augmenting Differentiable Simulators with Neural Networkscited by 77
  10. 2020Scalable Differentiable Physics for Learning and Controlcited by 72
  11. 2016Theano: A Python framework for fast computation of mathematical expressionscited by 64
  12. 2021DiffCloth: Differentiable Cloth Simulation with Dry Frictional Contactcited by 64
9 more
  1. 2018SPNets: Differentiable Fluid Dynamics for Deep Neural Networkscited by 62
  2. 2021DiffPD: Differentiable Projective Dynamicscited by 62
  3. 2021Fast and Feature-Complete Differentiable Physics for Articulated Rigid Bodies with Contactcited by 50
  4. 2021Efficient Differentiable Simulation of Articulated Bodiescited by 38
  5. 2022SAM-RL: Sensing-Aware Model-Based Reinforcement Learning via Differentiable Physics-Based Simulation and Renderingcited by 22
  6. 2021SAGCI-System: Towards Sample-Efficient, Generalizable, Compositional, and Incremental Robot Learningcited by 16
  7. 2017Automatic Differentiation of Rigid Body Dynamics for Optimal Control and Estimationcited by 11
  8. 2020A Large Scale Benchmark and an Inclusion-Based Algorithm for Continuous Collision Detectioncited by 3
  9. 2022Midas: A Multi-Joint Robotics Simulator with Intersection-Free Frictional Contactcited by 1

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Citing papers per year: 2023: 2; 2024: 2; 2025: 3.

7 papers.