text: 488,61,1506756429285191682,265421900,Eric Heiden,"We introduce a new method to learn simulators from depth and RGB videos. The ""URDF"" of an articulated rigid-body mechanism is reconstructed, and the parameters of the simulator inferred through Bayesian inference. Website: <LINK> Paper: <LINK> <LINK> Our pipeline leverages inverse rendering (nvdiffrast) and differentiable physics (Tiny Differentiable Simulator) to track objects in the scene, find articulations via a RANSAC approach, and infer the distribution over simulation parameters. <LINK> Our approach finds a digital twin for articulated mechanisms from real depth or RGB video. Check out our paper for more details! Joint work w/ Ziang Liu, @VibhavVineet, @erwincoumans, @gauravsukhatme <LINK>",https://arxiv.org/abs/2203.10488,"Being able to reproduce physical phenomena ranging from light interaction to contact mechanics, simulators are becoming increasingly useful in more and more application domains where real-world interaction or labeled data are difficult to obtain. Despite recent progress, significant human effort is needed to configure simulators to accurately reproduce real-world behavior. We introduce a pipeline that combines inverse rendering with differentiable simulation to create digital twins of real-world articulated mechanisms from depth or RGB videos. Our approach automatically discovers joint types and estimates their kinematic parameters, while the dynamic properties of the overall mechanism are tuned to attain physically accurate simulations. Control policies optimized in our derived simulation transfer successfully back to the original system, as we demonstrate on a simulated system. Further, our approach accurately reconstructs the kinematic tree of an articulated mechanism being manipulated by a robot, and highly nonlinear dynamics of a real-world coupled pendulum mechanism. Website: this https URL ",Inferring Articulated Rigid Body Dynamics from RGBD Video,3,"['We introduce a new method to learn simulators from depth and RGB videos. The ""URDF"" of an articulated rigid-body mechanism is reconstructed, and the parameters of the simulator inferred through Bayesian inference.\n\nWebsite: <LINK>\nPaper: <LINK> <LINK>', 'Our pipeline leverages inverse rendering (nvdiffrast) and differentiable physics (Tiny Differentiable Simulator) to track objects in the scene, find articulations via a RANSAC approach, and infer the distribution over simulation parameters. https://t.co/ccgVoJ9wWZ', 'Our approach finds a digital twin for articulated mechanisms from real depth or RGB video.\nCheck out our paper for more details!\n\nJoint work w/ Ziang Liu, @VibhavVineet, @erwincoumans, @gauravsukhatme https://t.co/8kehFh80AF']",22,03,705