102 citations · 132 across the 16 of their papers we have counts for
8 papers · 1 filter
DiAReL: Reinforcement Learning with Disturbance Awareness for Robust Sim2Real Policy Transfer in Robot Control
Mohammadhossein Malmir, Josip Josifovski, Noah Klarmann +1
Delayed Markov decision processes (DMDPs) fulfill the Markov property by augmenting the state space of agents with a finite time window of recently committed actions. In reliance o…
Language-Conditioned Imitation Learning with Base Skill Priors under Unstructured Data
Hongkuan Zhou, Zhenshan Bing, Xiangtong Yao +4
The growing interest in language-conditioned robot manipulation aims to develop robots capable of understanding and executing complex tasks, with the objective of enabling robots t…
Safety Guaranteed Manipulation Based on Reinforcement Learning Planner and Model Predictive Control Actor
Zhenshan Bing, Aleksandr Mavrichev, Sicong Shen +4
Deep reinforcement learning (RL) has been endowed with high expectations in tackling challenging manipulation tasks in an autonomous and self-directed fashion. Despite the signific…
A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors
Shangding Gu, Alap Kshirsagar, Yali Du +3
Deployment of Reinforcement Learning (RL) algorithms for robotics applications in the real world requires ensuring the safety of the robot and its environment. Safe Robot RL (SRRL)…
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…
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…
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…