Embodied Vision
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EKFPhys [
] filters object pose and friction parameter from deep learning-based object pose estimates in RGB-D images and using a differentiable physics simulation as state-transition model. ©~IEEE. Reprinted, with permission, from [].
Differentiable Physics for Scene Understanding
] filters object pose and friction parameter from deep learning-based object pose estimates in RGB-D images and using a differentiable physics simulation as state-transition model. ©~IEEE. Reprinted, with permission, from [].
In our work EKFPhys [], we use state-of-the-art 3D object detection and pose estimation (Lipson et al., Coupled Iterative Refinement for 6D Multi-Object Pose Estimation, CVPR 2022) to detect objects with known shape and texture in RGB-D images. The 6-DoF object pose (3D rotation and translation) is filtered together with the object's Coulomb friction parameter with the underlying surface using an extended Kalman filter (EKF). The filter uses the detected object poses as observations and the differentiable physics simulation as state-transition model. We propose novel synthetic and real benchmark datasets and evaluate the performance of our approach in estimating object pose and friction parameters.
], we use state-of-the-art 3D object detection and pose estimation (Lipson et al., Coupled Iterative Refinement for 6D Multi-Object Pose Estimation, CVPR 2022) to detect objects with known shape and texture in RGB-D images. The 6-DoF object pose (3D rotation and translation) is filtered together with the object's Coulomb friction parameter with the underlying surface using an extended Kalman filter (EKF). The filter uses the detected object poses as observations and the differentiable physics simulation as state-transition model. We propose novel synthetic and real benchmark datasets and evaluate the performance of our approach in estimating object pose and friction parameters.
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Embodied Vision
Embodied Vision
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Embodied Vision
Learning and Dynamical Systems
Empirical Inference
Conference Paper
Black-Box vs. Gray-Box: A Case Study on Learning Table Tennis Ball Trajectory Prediction with Spin and Impacts
Achterhold, J., Tobuschat, P., Ma, H., Büchler, D., Muehlebach, M., Stueckler, J.
In Conference on Learning for Dynamics and Control, 211:878-890, Proceedings of Machine Learning Research, (Editors: Nikolai Matni, Manfred Morari and George J. Pappa), PMLR, June 2023 (Published)
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