940 citations · 1.4k across the 33 of their papers we have counts for
34 papers
Evolving the Complete Muscle: Efficient Morphology-Control Co-design for Musculoskeletal Locomotion
Lidong Sun, Wentao Zhao, Ye Wang +2
Musculoskeletal robots offer intrinsic compliance and flexibility, providing a promising paradigm for versatile locomotion. However, existing research typically relies on models wi…
What Foundation Models can Bring for Robot Learning in Manipulation : A Survey
Dingzhe Li, Yixiang Jin, Yuhao Sun +11
The realization of universal robots is an ultimate goal of researchers. However, a key hurdle in achieving this goal lies in the robots' ability to manipulate objects in their unst…
Smooth Computation without Input Delay: Robust Tube-Based Model Predictive Control for Robot Manipulator Planning
Yu Luo, Qie Sima, Tianying Ji +3
Model Predictive Control (MPC) has exhibited remarkable capabilities in optimizing objectives and meeting constraints. However, the substantial computational burden associated with…
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…
Scene Graph for Embodied Exploration in Cluttered Scenario
Yuhong Deng, Qie Sima, Di Guo +3
The ability to handle objects in cluttered environment has been long anticipated by robotic community. However, most of works merely focus on manipulation instead of rendering hidd…
Sim2Real Object-Centric Keypoint Detection and Description
Chengliang Zhong, Chao Yang, Jinshan Qi +4
Keypoint detection and description play a central role in computer vision. Most existing methods are in the form of scene-level prediction, without returning the object classes of…
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…
Continual Learning with Recursive Gradient Optimization
Hao Liu, Huaping Liu
Learning multiple tasks sequentially without forgetting previous knowledge, called Continual Learning(CL), remains a long-standing challenge for neural networks. Most existing meth…
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…
Self-Supervised Learning by Estimating Twin Class Distributions
Feng Wang, Tao Kong, Rufeng Zhang +2
We present TWIST, a simple and theoretically explainable self-supervised representation learning method by classifying large-scale unlabeled datasets in an end-to-end way. We emplo…
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…
A novel multimodal fusion network based on a joint coding model for lane line segmentation
Zhenhong Zou, Xinyu Zhang, Huaping Liu +3
There has recently been growing interest in utilizing multimodal sensors to achieve robust lane line segmentation. In this paper, we introduce a novel multimodal fusion architectur…
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…
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…
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…