Fourier Transporter: Bi-Equivariant Robotic Manipulation in 3D
arxiv(2024)
摘要
Many complex robotic manipulation tasks can be decomposed as a sequence of
pick and place actions. Training a robotic agent to learn this sequence over
many different starting conditions typically requires many iterations or
demonstrations, especially in 3D environments. In this work, we propose Fourier
Transporter (FourTran) which leverages the two-fold SE(d)xSE(d) symmetry in the
pick-place problem to achieve much higher sample efficiency. FourTran is an
open-loop behavior cloning method trained using expert demonstrations to
predict pick-place actions on new environments. FourTran is constrained to
incorporate symmetries of the pick and place actions independently. Our method
utilizes a fiber space Fourier transformation that allows for memory-efficient
construction. We test our proposed network on the RLbench benchmark and achieve
state-of-the-art results across various tasks.
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