940 citations · 2k across the 39 of their papers we have counts for
40 papers
Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment
Yuhong Deng, Xiaofeng Guo, Yixuan Wei +5
In this paper, a novel robotic grasping system is established to automatically pick up objects in cluttered scenes. A composite robotic hand composed of a suction cup and a gripper…
Hybrid Robotic Grasping with a Soft Multimodal Gripper and a Deep Multistage Learning Scheme
Fukang Liu, Fuchun Sun, Bin Fang +3
Grasping has long been considered an important and practical task in robotic manipulation. Yet achieving robust and efficient grasps of diverse objects is challenging, since it inv…
Self-supervised 3D Semantic Representation Learning for Vision-and-Language Navigation
Sinan Tan, Mengmeng Ge, Di Guo +2
In the Vision-and-Language Navigation task, the embodied agent follows linguistic instructions and navigates to a specific goal. It is important in many practical scenarios and has…
Audio-Visual Grounding Referring Expression for Robotic Manipulation
Yefei Wang, Kaili Wang, Yi Wang +3
Referring expressions are commonly used when referring to a specific target in people's daily dialogue. In this paper, we develop a novel task of audio-visual grounding referring e…
Multi-Agent Embodied Visual Semantic Navigation with Scene Prior Knowledge
Xinzhu Liu, Di Guo, Huaping Liu +1
In visual semantic navigation, the robot navigates to a target object with egocentric visual observations and the class label of the target is given. It is a meaningful task inspir…
Knowledge-based Embodied Question Answering
Sinan Tan, Mengmeng Ge, Di Guo +2
In this paper, we propose a novel Knowledge-based Embodied Question Answering (K-EQA) task, in which the agent intelligently explores the environment to answer various questions wi…
Learning Deep Multimodal Feature Representation with Asymmetric Multi-layer Fusion
Yikai Wang, Fuchun Sun, Ming Lu +1
We propose a compact and effective framework to fuse multimodal features at multiple layers in a single network. The framework consists of two innovative fusion schemes. Firstly, u…
TransSC: Transformer-based Shape Completion for Grasp Evaluation
Wenkai Chen, Hongzhuo Liang, Zhaopeng Chen +2
Currently, robotic grasping methods based on sparse partial point clouds have attained a great grasping performance on various objects while they often generate wrong grasping cand…
Adversarial Option-Aware Hierarchical Imitation Learning
Mingxuan Jing, Wenbing Huang, Fuchun Sun +4
It has been a challenge to learning skills for an agent from long-horizon unannotated demonstrations. Existing approaches like Hierarchical Imitation Learning(HIL) are prone to com…
A Robust Tube-Based Smooth-MPC for Robot Manipulator Planning
Yu Luo, Mingxuan Jing, Tianying Ji +2
Model Predictive Control (MPC) has shown the great performance of target optimization and constraint satisfaction. However, the heavy computation of the Optimal Control Problem (OC…
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…
Elastic Interaction of Particles for Robotic Tactile Simulation
Yikai Wang, Wenbing Huang, Bin Fang +1
Tactile sensing plays an important role in robotic perception and manipulation. To overcome the real-world limitations of data collection, simulating tactile response in virtual en…
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…
Resolution Switchable Networks for Runtime Efficient Image Recognition
Yikai Wang, Fuchun Sun, Duo Li +1
We propose a general method to train a single convolutional neural network which is capable of switching image resolutions at inference. Thus the running speed can be selected to m…
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…
Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation
Runfa Chen, Wenbing Huang, Binghui Huang +2
Unsupervised image-to-image translation is a central task in computer vision. Current translation frameworks will abandon the discriminator once the training process is completed.…
A Mobile Robot Hand-Arm Teleoperation System by Vision and IMU
Shuang Li, Jiaxi Jiang, Philipp Ruppel +5
In this paper, we present a multimodal mobile teleoperation system that consists of a novel vision-based hand pose regression network (Transteleop) and an IMU-based arm tracking me…
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…
Robust Robotic Pouring using Audition and Haptics
Hongzhuo Liang, Chuangchuang Zhou, Shuang Li +6
Robust and accurate estimation of liquid height lies as an essential part of pouring tasks for service robots. However, vision-based methods often fail in occluded conditions while…
Reinforcement Learning from Imperfect Demonstrations under Soft Expert Guidance
Mingxuan Jing, Xiaojian Ma, Wenbing Huang +4
In this paper, we study Reinforcement Learning from Demonstrations (RLfD) that improves the exploration efficiency of Reinforcement Learning (RL) by providing expert demonstrations…
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement
Chao Yang, Xiaojian Ma, Wenbing Huang +4
This paper studies Learning from Observations (LfO) for imitation learning with access to state-only demonstrations. In contrast to Learning from Demonstration (LfD) that involves…
Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction
Yikai Wang, Liang Zhang, Quanyu Dai +5
Improving the performance of click-through rate (CTR) prediction remains one of the core tasks in online advertising systems. With the rise of deep learning, CTR prediction models…
Deep Point-wise Prediction for Action Temporal Proposal
Luxuan Li, Tao Kong, Fuchun Sun +1
Detecting actions in videos is an important yet challenging task. Previous works usually utilize (a) sliding window paradigms, or (b) per-frame action scoring and grouping to enume…