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Found 11 papers, 2 papers with code
Relevance-driven Decision Making for Safer and More Efficient Human Robot Collaboration
In this paper, we further developed a novel two-loop framework integrating real-time and asynchronous processing to quantify relevance and apply relevance for safer and more efficient HRC.
Relevance for Human Robot Collaboration
Inspired by the human ability to selectively focus on relevant information, this paper introduces relevance, a novel dimensionality reduction process for human-robot collaboration (HRC).
ISR: Invertible Symbolic Regression
The proposed ISR method naturally combines the principles of Invertible Neural Networks (INNs) and Equation Learner (EQL), a neural network-based symbolic architecture for function learning.
Data-Driven Discovery of PDEs via the Adjoint Method
Using variational calculus, we obtain an evolution equation for the Lagrange multipliers (adjoint equations) allowing us to compute the gradient of the objective function with respect to the parameters of PDEs given data in a straightforward manner.
Enhanced Human-Robot Collaboration using Constrained Probabilistic Human-Motion Prediction
We propose a novel human motion prediction framework that incorporates human joint constraints and scene constraints in a Gaussian Process Regression (GPR) model to predict human motion over a set time horizon.
MESSY Estimation: Maximum-Entropy based Stochastic and Symbolic densitY Estimation
We validate the proposed MESSY estimation method against other benchmark methods for the case of a bi-modal and a discontinuous density, as well as a density at the limit of physical realizability.
GSR: A Generalized Symbolic Regression Approach
Identifying the mathematical relationships that best describe a dataset remains a very challenging problem in machine learning, and is known as Symbolic Regression (SR).
Reliable Neural Networks for Regression Uncertainty Estimation
While deep neural networks are highly performant and successful in a wide range of real-world problems, estimating their predictive uncertainty remains a challenging task.
D-DARTS: Distributed Differentiable Architecture Search
This approach is accompanied by a novel metric that measures the distance between architectures inside our custom search space.
Instance Semantic Segmentation Benefits from Generative Adversarial Networks
Rethinking reconstruction networks as a generator, we define the problem of predicting masks as a GANs game framework: A segmentation network generates the masks, and a discriminator network decides on the quality of the masks.
Cyclic Graph Dynamic Multilayer Perceptron for Periodic Signals
However, it is difficult to perfectly control the collection timing of the measurements.
