8 citations · 21 across the 11 of their papers we have counts for
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cs.RO2023
CEMSSL: Conditional Embodied Self-Supervised Learning is All You Need for High-precision Multi-solution Inverse Kinematics of Robot Arms
Qu Weiming, Liu Tianlin, Du Jiawei +1
In the field of signal processing for robotics, the inverse kinematics of robot arms presents a significant challenge due to multiple solutions caused by redundant degrees of freed…
cs.RO2023★ 1 cited
High-Precise Robot Arm Manipulation based on Online Iterative Learning and Forward Simulation with Positioning Error Below End-Effector Physical Minimum Displacement
Qu Weiming, Liu Tianlin, Luo Dingsheng
Precision is a crucial performance indicator for robot arms, as high precision manipulation allows for a wider range of applications. Traditional methods for improving robot arm pr…
cs.RO2023
Embodied Self-Supervised Learning (EMSSL) with Sampling and Training Coordination for Robot Arm Inverse Kinematics Model Learning
Qu Weiming, Liu Tianlin, Wu Xihong +1
Forward and inverse kinematics models are fundamental to robot arms, serving as the basis for the robot arm's operational tasks. However, in model learning of robot arms, especiall…