Parallel learning: Overview and perspective for computational learning across Syn2Real and Sim2Real
Q Miao, Y Lv, M Huang, X Wang… - IEEE/CAA Journal of …, 2023 - ieeexplore.ieee.org
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 …
Transfer learning in robotics: An upcoming breakthrough? A review of promises and challenges
Transfer learning is a conceptually-enticing paradigm in pursuit of truly intelligent embodied
agents. The core concept—reusing prior knowledge to learn in and from novel situations—is …
agents. The core concept—reusing prior knowledge to learn in and from novel situations—is …
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 …
Robot learning in the era of foundation models: A survey
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 …
GATSBI: Generative adversarial training for simulation-based inference
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 …
Constrained reinforcement learning using distributional representation for trustworthy quadrotor UAV tracking control
Simultaneously accurate and reliable tracking control for quadrotors in complex dynamic
environments is challenging. The chaotic nature of aerodynamics, derived from drag forces …
environments is challenging. The chaotic nature of aerodynamics, derived from drag forces …
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 …
Beyond simulation: Unlocking the frontiers of humanoid robot capability and intelligence with Pepper's open-source digital twin
This research paper presents a high-fidelity, open-source digital-twin of the Pepper robot
developed within the framework of the Robot Operating System 2 (ROS 2) for better …
developed within the framework of the Robot Operating System 2 (ROS 2) for better …