90 citations · 133 across the 12 of their papers we have counts for
5 papers · 1 filter
Fault-Aware Robust Control via Adversarial Reinforcement Learning
Fan Yang, Chao Yang, Di Guo +2
Robots have limited adaptation ability compared to humans and animals in the case of damage. However, robot damages are prevalent in real-world applications, especially for robots…
Adversarial Skill Learning for Robust Manipulation
Pingcheng Jian, Chao Yang, Di Guo +2
Deep reinforcement learning has made significant progress in robotic manipulation tasks and it works well in the ideal disturbance-free environment. However, in a real-world enviro…
Unsupervised Representation Learning by InvariancePropagation
Feng Wang, Huaping Liu, Di Guo +1
Unsupervised learning methods based on contrastive learning have drawn increasing attention and achieved promising results. Most of them aim to learn representations invariant to i…
Towards Embodied Scene Description
Sinan Tan, Huaping Liu, Di Guo +2
Embodiment is an important characteristic for all intelligent agents (creatures and robots), while existing scene description tasks mainly focus on analyzing images passively and t…
MQA: Answering the Question via Robotic Manipulation
Yuhong Deng, Di Guo, Xiaofeng Guo +3
In this paper, we propose a novel task, Manipulation Question Answering (MQA), where the robot performs manipulation actions to change the environment in order to answer a given qu…