940 citations · 1.4k across the 33 of their papers we have counts for
8 papers · 1 filter
Understanding the Behaviour of Contrastive Loss
Feng Wang, Huaping Liu
Unsupervised contrastive learning has achieved outstanding success, while the mechanism of contrastive loss has been less studied. In this paper, we concentrate on the understandin…
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…
DeepSSM: Deep State-Space Model for 3D Human Motion Prediction
Xiaoli Liu, Jianqin Yin, Huaping Liu +1
Predicting future human motion plays a significant role in human-machine interactions for various real-life applications. A unified formulation and multi-order modeling are two cri…
Energy-based Periodicity Mining with Deep Features for Action Repetition Counting in Unconstrained Videos
Jianqin Yin, Yanchun Wu, Huaping Liu +3
Action repetition counting is to estimate the occurrence times of the repetitive motion in one action, which is a relatively new, important but challenging measurement problem. To…
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…