Driven by data or derived through physics? a review of hybrid physics guided machine learning techniques with cyber-physical system (cps) focus

R Rai, CK Sahu - IEEe Access, 2020‏ - ieeexplore.ieee.org
A multitude of cyber-physical system (CPS) applications, including design, control,
diagnosis, prognostics, and a host of other problems, are predicated on the assumption of …

A review of robot learning for manipulation: Challenges, representations, and algorithms

O Kroemer, S Niekum, G Konidaris - Journal of machine learning research, 2021‏ - jmlr.org
A key challenge in intelligent robotics is creating robots that are capable of directly
interacting with the world around them to achieve their goals. The last decade has seen …

Tossingbot: Learning to throw arbitrary objects with residual physics

A Zeng, S Song, J Lee, A Rodriguez… - IEEE Transactions on …, 2020‏ - ieeexplore.ieee.org
We investigate whether a robot arm can learn to pick and throw arbitrary rigid objects into
selected boxes quickly and accurately. Throwing has the potential to increase the physical …

[HTML][HTML] A survey of robot manipulation in contact

M Suomalainen, Y Karayiannidis, V Kyrki - Robotics and Autonomous …, 2022‏ - Elsevier
In this survey, we present the current status on robots performing manipulation tasks that
require varying contact with the environment, such that the robot must either implicitly or …

Physgen: Rigid-body physics-grounded image-to-video generation

S Liu, Z Ren, S Gupta, S Wang - European Conference on Computer …, 2024‏ - Springer
We present PhysGen, a novel image-to-video generation method that converts a single
image and an input condition (eg., force and torque applied to an object in the image) to …

Conceptualizing digital twins

R Eramo, F Bordeleau, B Combemale… - IEEE …, 2021‏ - ieeexplore.ieee.org
Properly arranging models, data sources, and their relations to engineer digital twins is
challenging. We propose a conceptual modeling framework for digital twins that captures the …

NeuralSim: Augmenting differentiable simulators with neural networks

E Heiden, D Millard, E Coumans… - … on Robotics and …, 2021‏ - ieeexplore.ieee.org
Differentiable simulators provide an avenue for closing the sim-to-real gap by enabling the
use of efficient, gradient-based optimization algorithms to find the simulation parameters that …

Residual policy learning

T Silver, K Allen, J Tenenbaum, L Kaelbling - arxiv preprint arxiv …, 2018‏ - arxiv.org
We present Residual Policy Learning (RPL): a simple method for improving
nondifferentiable policies using model-free deep reinforcement learning. RPL thrives in …

Incorporating physics into data-driven computer vision

A Kadambi, C de Melo, CJ Hsieh… - Nature Machine …, 2023‏ - nature.com
Many computer vision techniques infer properties of our physical world from images.
Although images are formed through the physics of light and mechanics, computer vision …

Modeling of deformable objects for robotic manipulation: A tutorial and review

VE Arriola-Rios, P Guler, F Ficuciello… - Frontiers in Robotics …, 2020‏ - frontiersin.org
Manipulation of deformable objects has given rise to an important set of open problems in
the field of robotics. Application areas include robotic surgery, household robotics …