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20162023
most citedDeep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment

90 citations · 270 across the 18 of their papers we have counts for

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11 papers · 1 filter

cs.RO2023★ 90 cited

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…

cs.RO2021★ 1 cited

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…

cs.RO2021

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…

cs.RO2021

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…

cs.RO2020★ 2 cited

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…

cs.RO2020★ 5 cited

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…