Parallel learning: Overview and perspective for computational learning across Syn2Real and Sim2Real
The virtual-to-real paradigm, ie, training models on virtual data and then applying them to
solve real-world problems, has attracted more and more attention from various domains by …
solve real-world problems, has attracted more and more attention from various domains by …
Artificial neural networks for photonic applications—from algorithms to implementation: tutorial
This tutorial–review on applications of artificial neural networks in photonics targets a broad
audience, ranging from optical research and engineering communities to computer science …
audience, ranging from optical research and engineering communities to computer science …
Learning agile soccer skills for a bipedal robot with deep reinforcement learning
We investigated whether deep reinforcement learning (deep RL) is able to synthesize
sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be …
sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be …
Sim-to-lab-to-real: Safe reinforcement learning with shielding and generalization guarantees
Safety is a critical component of autonomous systems and remains a challenge for learning-
based policies to be utilized in the real world. In particular, policies learned using …
based policies to be utilized in the real world. In particular, policies learned using …
GATSBI: Generative adversarial training for simulation-based inference
P Ramesh, JM Lueckmann, J Boelts… - arXiv preprint arXiv …, 2022 - arxiv.org
Simulation-based inference (SBI) refers to statistical inference on stochastic models for
which we can generate samples, but not compute likelihoods. Like SBI algorithms …
which we can generate samples, but not compute likelihoods. Like SBI algorithms …
Robot learning in the era of foundation models: A survey
X Xiao, J Liu, Z Wang, Y Zhou, Y Qi, Q Cheng… - arXiv preprint arXiv …, 2023 - arxiv.org
The proliferation of Large Language Models (LLMs) has s fueled a shift in robot learning
from automation towards general embodied Artificial Intelligence (AI). Adopting foundation …
from automation towards general embodied Artificial Intelligence (AI). Adopting foundation …
On the role of the action space in robot manipulation learning and sim-to-real transfer
We study the choice of action space in robot manipulation learning and sim-to-real transfer.
We define metrics that assess the performance, and examine the emerging properties in the …
We define metrics that assess the performance, and examine the emerging properties in the …
Sim-to-real deep reinforcement learning for safe end-to-end planning of aerial robots
In this study, a novel end-to-end path planning algorithm based on deep reinforcement
learning is proposed for aerial robots deployed in dense environments. The learning agent …
learning is proposed for aerial robots deployed in dense environments. The learning agent …
Adaptive robotic information gathering via non-stationary Gaussian processes
Robotic Information Gathering (RIG) is a foundational research topic that answers how a
robot (team) collects informative data to efficiently build an accurate model of an unknown …
robot (team) collects informative data to efficiently build an accurate model of an unknown …
[HTML][HTML] Stochastic optimal well control in subsurface reservoirs using reinforcement learning
A Dixit, AH ElSheikh - Engineering Applications of Artificial Intelligence, 2022 - Elsevier
We present a case study of model-free reinforcement learning (RL) framework to solve
stochastic optimal control for a predefined parameter uncertainty distribution and partially …
stochastic optimal control for a predefined parameter uncertainty distribution and partially …