66 citations · 353 across the 14 of their papers we have counts for
16 papers
Sim-to-Real Model-Based and Model-Free Deep Reinforcement Learning for Tactile Pushing
Max Yang, Yijiong Lin, Alex Church +4
Object pushing presents a key non-prehensile manipulation problem that is illustrative of more complex robotic manipulation tasks. While deep reinforcement learning (RL) methods ha…
Modelling of physical systems with a Hopf bifurcation using mechanistic models and machine learning
K. H. Lee, D. A. W. Barton, L. Renson
We propose a new hybrid modelling approach that combines a mechanistic model with a machine-learnt model to predict the limit cycle oscillations of physical systems with a Hopf bif…
Uncertainty quantification in mechanistic epidemic models via cross-entropy approximate Bayesian computation
Americo Cunha, David A. W. Barton, Thiago G. Ritto
This paper proposes a data-driven approximate Bayesian computation framework for parameter estimation and uncertainty quantification of epidemic models, which incorporates two nove…
Using scientific machine learning for experimental bifurcation analysis of dynamic systems
Sandor Beregi, David A. W. Barton, Djamel Rezgui +1
Augmenting mechanistic ordinary differential equation (ODE) models with machine-learnable structures is an novel approach to create highly accurate, low-dimensional models of engin…
Reduced-order modelling of flutter oscillations using normal forms and scientific machine learning
K. H. Lee, D. A. W. Barton, L. Renson
This paper introduces a machine learning approach to take a nonlinear differential-equation model that exhibits qualitative agreement with a physical experiment over a range of par…
Walking on TacTip toes: A tactile sensing foot for walking robots
Elizabeth A. Stone, Nathan F. Lepora, David A. W. Barton
Little research into tactile feet has been done for walking robots despite the benefits such feedback could give when walking on uneven terrain. This paper describes the developmen…