9 citations · 10 across the 5 of their papers we have counts for
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cs.RO2024
Discrete Policy: Learning Disentangled Action Space for Multi-Task Robotic Manipulation
Kun Wu, Yichen Zhu, Jinming Li +4
Learning visuomotor policy for multi-task robotic manipulation has been a long-standing challenge for the robotics community. The difficulty lies in the diversity of action space:…
cs.RO2024
Efficient Training of Generalizable Visuomotor Policies via Control-Aware Augmentation
Yinuo Zhao, Kun Wu, Tianjiao Yi +5
Improving generalization is one key challenge in embodied AI, where obtaining large-scale datasets across diverse scenarios is costly. Traditional weak augmentations, such as cropp…
cs.RO2024★ 1 cited
Learning from Imperfect Demonstrations with Self-Supervision for Robotic Manipulation
Kun Wu, Ning Liu, Zhen Zhao +5
Improving data utilization, especially for imperfect data from task failures, is crucial for robotic manipulation due to the challenging, time-consuming, and expensive data collect…