102 citations · 132 across the 16 of their papers we have counts for
7 papers · 1 filter
Autonomous Driving Simulator based on Neurorobotics Platform
Wei Cao, Liguo Zhou, Yuhong Huang +1
There are many artificial intelligence algorithms for autonomous driving, but directly installing these algorithms on vehicles is unrealistic and expensive. At the same time, many…
Meta-Reinforcement Learning via Language Instructions
Zhenshan Bing, Alexander Koch, Xiangtong Yao +2
Although deep reinforcement learning has recently been very successful at learning complex behaviors, it requires a tremendous amount of data to learn a task. One of the fundamenta…
Learning from Symmetry: Meta-Reinforcement Learning with Symmetrical Behaviors and Language Instructions
Xiangtong Yao, Zhenshan Bing, Genghang Zhuang +4
Meta-reinforcement learning (meta-RL) is a promising approach that enables the agent to learn new tasks quickly. However, most meta-RL algorithms show poor generalization in multi-…
Sim-to-Real Transfer of Robotic Assembly with Visual Inputs Using CycleGAN and Force Control
Chengjie Yuan, Yunlei Shi, Qian Feng +4
Recently, deep reinforcement learning (RL) has shown some impressive successes in robotic manipulation applications. However, training robots in the real world is nontrivial owing…
Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments
Rui Song, Dai Liu, Dave Zhenyu Chen +4
In federated learning, all networked clients contribute to the model training cooperatively. However, with model sizes increasing, even sharing the trained partial models often lea…
Analysis of Randomization Effects on Sim2Real Transfer in Reinforcement Learning for Robotic Manipulation Tasks
Josip Josifovski, Mohammadhossein Malmir, Noah Klarmann +3
Randomization is currently a widely used approach in Sim2Real transfer for data-driven learning algorithms in robotics. Still, most Sim2Real studies report results for a specific r…
A Review of Safe Reinforcement Learning: Methods, Theory and Applications
Shangding Gu, Long Yang, Yali Du +4
Reinforcement Learning (RL) has achieved tremendous success in many complex decision-making tasks. However, safety concerns are raised during deploying RL in real-world application…