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Found 7 papers, 3 papers with code
Feedforward Control in the Presence of Input Nonlinearities: A Learning-based Approach
Advanced feedforward control methods enable mechatronic systems to perform varying motion tasks with extreme accuracy and throughput.
Is Vanilla Policy Gradient Overlooked? Analyzing Deep Reinforcement Learning for Hanabi
In pursuit of enhanced multi-agent collaboration, we analyze several on-policy deep reinforcement learning algorithms in the recently published Hanabi benchmark.
Position-Dependent Snap Feedforward: A Gaussian Process Framework
Position-dependent compliance is compensated for by using a Gaussian process to model the snap feedforward parameter as a continuous function of position.
Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder
Mechatronic systems have increasingly stringent performance requirements for motion control, leading to a situation where many factors, such as position-dependency, cannot be neglected in feedforward control.
Learning nonlinear feedforward: a Gaussian Process Approach Applied to a Printer with Friction
Feedforward control is essential to achieving good tracking performance in positioning systems.
PDE-based Group Equivariant Convolutional Neural Networks
We solve the PDE of interest by a combination of linear group convolutions and non-linear morphological group convolutions with analytic kernel approximations that we underpin with formal theorems.
SciSports: Learning football kinematics through two-dimensional tracking data
After training, the Variational Autoencoders generated trajectories that are difficult to distinguish, visually, from the data.
