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cs.RO2023
On Robot Grasp Learning Using Equivariant Models
Xupeng Zhu, Dian Wang, Guanang Su +3
Real-world grasp detection is challenging due to the stochasticity in grasp dynamics and the noise in hardware. Ideally, the system would adapt to the real world by training direct…
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
Sample Efficient Grasp Learning Using Equivariant Models
Xupeng Zhu, Dian Wang, Ondrej Biza +3
In planar grasp detection, the goal is to learn a function from an image of a scene onto a set of feasible grasp poses in . In this paper, we recognize that the opt…