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[1] Andrew J. Davison,et al. Sim-to-Real Reinforcement Learning for Deformable Object Manipulation , 2018, CoRL.
[2] Joshua B. Tenenbaum,et al. A Compositional Object-Based Approach to Learning Physical Dynamics , 2016, ICLR.
[3] Emanuel Todorov,et al. Trajectory optimization for domains with contacts using inverse dynamics , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[4] Jiancheng Liu,et al. ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics , 2018, 2019 International Conference on Robotics and Automation (ICRA).
[5] Herke van Hoof,et al. Addressing Function Approximation Error in Actor-Critic Methods , 2018, ICML.
[6] Chenfanfu Jiang,et al. The material point method for simulating continuum materials , 2016, SIGGRAPH Courses.
[7] Pieter Abbeel,et al. Learning to Manipulate Deformable Objects without Demonstrations , 2019, Robotics: Science and Systems.
[8] Ali Farhadi,et al. AI2-THOR: An Interactive 3D Environment for Visual AI , 2017, ArXiv.
[9] Joshua B. Tenenbaum,et al. The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark Towards Physically Realistic Embodied AI , 2021, 2022 International Conference on Robotics and Automation (ICRA).
[10] Frédo Durand,et al. DiffTaichi: Differentiable Programming for Physical Simulation , 2020, ICLR.
[11] Chenfanfu Jiang,et al. The affine particle-in-cell method , 2015, ACM Trans. Graph..
[12] Jonas Degrave,et al. A DIFFERENTIABLE PHYSICS ENGINE FOR DEEP LEARNING IN ROBOTICS , 2016, Front. Neurorobot..
[13] Ming C. Lin,et al. Scalable Differentiable Physics for Learning and Control , 2020, ICML.
[14] Eftychios Sifakis,et al. An adaptive generalized interpolation material point method for simulating elastoplastic materials , 2017, ACM Trans. Graph..
[15] Pieter Abbeel,et al. Benchmarking Deep Reinforcement Learning for Continuous Control , 2016, ICML.
[16] Davide Scaramuzza,et al. Fast Trajectory Optimization for Agile Quadrotor Maneuvers with a Cable-Suspended Payload , 2017, Robotics: Science and Systems.
[17] Jitendra Malik,et al. Habitat: A Platform for Embodied AI Research , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[18] Frédo Durand,et al. Taichi , 2019, ACM Trans. Graph..
[19] Eftychios Sifakis,et al. Computing the Singular Value Decomposition of 3x3 matrices with minimal branching and elementary floating point operations , 2011 .
[20] Andre Pradhana,et al. Drucker-prager elastoplasticity for sand animation , 2016, ACM Trans. Graph..
[21] Marc G. Bellemare,et al. The Arcade Learning Environment: An Evaluation Platform for General Agents (Extended Abstract) , 2012, IJCAI.
[22] Ming C. Lin,et al. Differentiable Cloth Simulation for Inverse Problems , 2019, NeurIPS.
[23] Alec Radford,et al. Proximal Policy Optimization Algorithms , 2017, ArXiv.
[24] Russ Tedrake,et al. A direct method for trajectory optimization of rigid bodies through contact , 2014, Int. J. Robotics Res..
[25] Alexey Stomakhin,et al. A material point method for snow simulation , 2013, ACM Trans. Graph..
[26] Sanja Fidler,et al. VirtualHome: Simulating Household Activities Via Programs , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[27] Roi Poranne,et al. Trajectory Optimization for Cable-Driven Soft Robot Locomotion , 2019, Robotics: Science and Systems.
[28] Connor Schenck,et al. SPNets: Differentiable Fluid Dynamics for Deep Neural Networks , 2018, CoRL.
[29] Danfei Xu,et al. Folding deformable objects using predictive simulation and trajectory optimization , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[30] Gaurav S. Sukhatme,et al. Interactive Differentiable Simulation , 2019, ArXiv.
[31] Yang Wang,et al. Optimizing Intersection-Over-Union in Deep Neural Networks for Image Segmentation , 2016, ISVC.
[32] Chuang Gan,et al. CLEVRER: CoLlision Events for Video REpresentation and Reasoning , 2020, ICLR.
[33] Cosimo Della Santina,et al. Dynamic control of soft robots interacting with the environment , 2018, 2018 IEEE International Conference on Soft Robotics (RoboSoft).
[34] Yuval Tassa,et al. MuJoCo: A physics engine for model-based control , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[35] Joshua B. Tenenbaum,et al. End-to-End Differentiable Physics for Learning and Control , 2018, NeurIPS.
[36] Vijay Kumar,et al. Trajectory generation and control of a quadrotor with a cable-suspended load - A differentially-flat hybrid system , 2013, 2013 IEEE International Conference on Robotics and Automation.
[37] Leonidas J. Guibas,et al. SAPIEN: A SimulAted Part-Based Interactive ENvironment , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Vijay Kumar,et al. Mixed Integer Quadratic Program trajectory generation for a quadrotor with a cable-suspended payload , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).
[39] Andre Pradhana,et al. A moving least squares material point method with displacement discontinuity and two-way rigid body coupling , 2018, ACM Trans. Graph..
[40] Vladlen Koltun,et al. Learning to Control PDEs with Differentiable Physics , 2020, ICLR.
[41] Chuang Gan,et al. ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation , 2020, ArXiv.
[42] Jiajun Wu,et al. Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids , 2018, ICLR.
[43] Jeannette Bohg,et al. Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objects , 2020, IEEE Robotics and Automation Letters.
[44] J. Teran,et al. Dynamic anticrack propagation in snow , 2018, Nature Communications.
[45] Daniela Rus,et al. Dynamics and trajectory optimization for a soft spatial fluidic elastomer manipulator , 2016, Int. J. Robotics Res..
[46] Sergey Levine,et al. Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor , 2018, ICML.
[47] D. Sulsky. Erratum: Application of a particle-in-cell method to solid mechanics , 1995 .
[48] Bo Ren,et al. Fluid directed rigid body control using deep reinforcement learning , 2018, ACM Trans. Graph..
[49] Razvan Pascanu,et al. Interaction Networks for Learning about Objects, Relations and Physics , 2016, NIPS.
[50] Bernhard Thomaszewski,et al. ADD , 2020, ACM Trans. Graph..
[51] Cecilia Laschi,et al. Control Strategies for Soft Robotic Manipulators: A Survey. , 2018, Soft robotics.
[52] Daniel L. K. Yamins,et al. Flexible Neural Representation for Physics Prediction , 2018, NeurIPS.