4 citations · 5 across the 8 of their papers we have counts for
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cs.RO2022
SEIL: Simulation-augmented Equivariant Imitation Learning
Mingxi Jia, Dian Wang, Guanang Su +4
In robotic manipulation, acquiring samples is extremely expensive because it often requires interacting with the real world. Traditional image-level data augmentation has shown the…
cs.RO2022★ 1 cited
BulletArm: An Open-Source Robotic Manipulation Benchmark and Learning Framework
Dian Wang, Colin Kohler, Xupeng Zhu +2
We present BulletArm, a novel benchmark and learning-environment for robotic manipulation. BulletArm is designed around two key principles: reproducibility and extensibility. We ai…
cs.RO2022★ 4 cited
On-Robot Learning With Equivariant Models
Dian Wang, Mingxi Jia, Xupeng Zhu +2
Recently, equivariant neural network models have been shown to improve sample efficiency for tasks in computer vision and reinforcement learning. This paper explores this idea in t…