Offline reinforcement learning: Tutorial, review, and perspectives on open problems
In this tutorial article, we aim to provide the reader with the conceptual tools needed to get
started on research on offline reinforcement learning algorithms: reinforcement learning …
started on research on offline reinforcement learning algorithms: reinforcement learning …
A review of physics simulators for robotic applications
The use of simulators in robotics research is widespread, underpinning the majority of recent
advances in the field. There are now more options available to researchers than ever before …
advances in the field. There are now more options available to researchers than ever before …
Learning agile robotic locomotion skills by imitating animals
Reproducing the diverse and agile locomotion skills of animals has been a longstanding
challenge in robotics. While manually-designed controllers have been able to emulate many …
challenge in robotics. While manually-designed controllers have been able to emulate many …
A survey on learning-based robotic grasping
Abstract Purpose of Review This review provides a comprehensive overview of machine
learning approaches for vision-based robotic grasping and manipulation. Current trends and …
learning approaches for vision-based robotic grasping and manipulation. Current trends and …
An introduction to deep reinforcement learning
Deep reinforcement learning is the combination of reinforcement learning (RL) and deep
learning. This field of research has been able to solve a wide range of complex …
learning. This field of research has been able to solve a wide range of complex …
[图书][B] Synthetic data for deep learning
SI Nikolenko - 2021 - Springer
You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …
anymore? Anyway, you are reading this, and it means that I have managed to release one of …
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 …
Sim-to-real via sim-to-sim: Data-efficient robotic grasping via randomized-to-canonical adaptation networks
S James, P Wohlhart, M Kalakrishnan… - Proceedings of the …, 2019 - openaccess.thecvf.com
Real world data, especially in the domain of robotics, is notoriously costly to collect. One way
to circumvent this can be to leverage the power of simulation to produce large amounts of …
to circumvent this can be to leverage the power of simulation to produce large amounts of …
Identifying the risks of lm agents with an lm-emulated sandbox
Recent advances in Language Model (LM) agents and tool use, exemplified by applications
like ChatGPT Plugins, enable a rich set of capabilities but also amplify potential risks-such …
like ChatGPT Plugins, enable a rich set of capabilities but also amplify potential risks-such …
Stabilizing deep q-learning with convnets and vision transformers under data augmentation
While agents trained by Reinforcement Learning (RL) can solve increasingly challenging
tasks directly from visual observations, generalizing learned skills to novel environments …
tasks directly from visual observations, generalizing learned skills to novel environments …