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Go fetch!-dynamic grasps using boston dynamics spot with external robotic arm
S Zimmermann, R Poranne… - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
We combine Boston Dynamics Spot® with a light-weight, external robot arm to perform
dynamic gras** maneuvers. While Spot is a reliable, robust and easy-to-control mobile …
dynamic gras** maneuvers. While Spot is a reliable, robust and easy-to-control mobile …
Neural networks with physics-informed architectures and constraints for dynamical systems modeling
Effective inclusion of physics-based knowledge into deep neural network models of
dynamical systems can greatly improve data efficiency and generalization. Such a priori …
dynamical systems can greatly improve data efficiency and generalization. Such a priori …
Combining physics and deep learning to learn continuous-time dynamics models
Deep learning has been widely used within learning algorithms for robotics. One
disadvantage of deep networks is that these networks are black-box representations …
disadvantage of deep networks is that these networks are black-box representations …
Lagrangian operator inference enhanced with structure-preserving machine learning for nonintrusive model reduction of mechanical systems
Complex mechanical systems often exhibit strongly nonlinear behavior due to the presence
of nonlinearities in the energy dissipation mechanisms, material constitutive relationships, or …
of nonlinearities in the energy dissipation mechanisms, material constitutive relationships, or …
Investigating compounding prediction errors in learned dynamics models
Accurately predicting the consequences of agents' actions is a key prerequisite for planning
in robotic control. Model-based reinforcement learning (MBRL) is one paradigm which relies …
in robotic control. Model-based reinforcement learning (MBRL) is one paradigm which relies …
Deconstructing the inductive biases of hamiltonian neural networks
Physics-inspired neural networks (NNs), such as Hamiltonian or Lagrangian NNs,
dramatically outperform other learned dynamics models by leveraging strong inductive …
dramatically outperform other learned dynamics models by leveraging strong inductive …
Compositional learning of dynamical system models using port-Hamiltonian neural networks
Many dynamical systems—from robots interacting with their surroundings to large-scale
multi-physics systems—involve a number of interacting subsystems. Toward the objective of …
multi-physics systems—involve a number of interacting subsystems. Toward the objective of …
Encoding physical constraints in differentiable newton-euler algorithm
Abstract The recursive Newton-Euler Algorithm (RNEA) is a popular technique in robotics for
computing the dynamics of robots. The computed dynamics can then be used for torque …
computing the dynamics of robots. The computed dynamics can then be used for torque …
Preserving Lagrangian structure in data-driven reduced-order modeling of large-scale dynamical systems
This work presents a nonintrusive physics-preserving method to learn reduced-order models
(ROMs) of Lagrangian systems, which includes nonlinear wave equations. Existing intrusive …
(ROMs) of Lagrangian systems, which includes nonlinear wave equations. Existing intrusive …
Learning modular simulations for homogeneous systems
Complex systems are often decomposed into modular subsystems for engineering
tractability. Although various equation based white-box modeling techniques make use of …
tractability. Although various equation based white-box modeling techniques make use of …