90 citations · 155 across the 13 of their papers we have counts for
14 papers
CompetEvo: Towards Morphological Evolution from Competition
Kangyao Huang, Di Guo, Xinyu Zhang +2
Training an agent to adapt to specific tasks through co-optimization of morphology and control has widely attracted attention. However, whether there exists an optimal configuratio…
Stimulate the Potential of Robots via Competition
Kangyao Huang, Di Guo, Xinyu Zhang +2
It is common for us to feel pressure in a competition environment, which arises from the desire to obtain success comparing with other individuals or opponents. Although we might g…
Heterogeneous Embodied Multi-Agent Collaboration
Xinzhu Liu, Di Guo, Huaping Liu
Multi-agent embodied tasks have recently been studied in complex indoor visual environments. Collaboration among multiple agents can improve work efficiency and has significant pra…
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…
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…
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…