Fourier Transporter: Bi-Equivariant Robotic Manipulation in 3D
Haojie Huang, Owen Howell, Dian Wang, Xupeng Zhu, Robin Walters,, Robert Platt
TL;DR
Fourier Transporter (FourTran) leverages symmetries in 3D pick-and-place robotic tasks to significantly improve sample efficiency and achieves state-of-the-art performance on the RLbench benchmark.
Contribution
The paper introduces FourTran, a novel Fourier-based method that exploits SE(d)xSE(d) symmetry in pick-and-place tasks for improved sample efficiency in robotic manipulation.
Findings
Achieves state-of-the-art results on RLbench benchmark.
Utilizes fiber space Fourier transformation for memory-efficient learning.
Leverages symmetries to enhance sample efficiency in 3D manipulation tasks.
Abstract
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.
Peer Reviews
Decision·ICLR 2024 poster
- The paper proposes FOURTRAN for leveraging bi-equivariant structure in manipulation pick-place problems in 2D and 3D. - The paper presents a theoretical framework for exploiting bi-equivariant symmetry. It contains proofs for propositions that address the symmetry constraints and properties of the model.
- The current model is limited in a single-task setting, while the baseline methods are designed for multi-task purposes. I'm concerned that the comparisons may not be fair. - It relies solely on open-loop control, disregarding path planning and collision awareness. - The paper is not well-written and some of the terms are difficult to understand. It uses a lot of notations, but many of them are not explained. - There are no real robot experiments.
The argument for a bi-equivariant policy is compelling. The use of Wigner D-matrices to represent an output distribution is very clever and (to my limited knowledge of the literature) seems novel. Their use in the place network to generate fast cross-correlations for bi-equivariance is definitely novel. All theory is well presented and seems well-backed, if a little dense at times to readers less versed in differential geometry and representation theory. Empirical results are extremely compelli
Weaknesses mostly center around presentation: the paper contains a lot of dense jargon, which is understandable given the material but could be improved: - Given that the Wigner D-matrix representation and corresponding 3D Fourier transform is the key insight that allows this action representation to work, it would be worth spending some more time to describe them in more detail - Some pseudocode/method description would be welcome Otherwise, further analysis of the representations introduced
The paper shows novelty in the use of Fourier transformation in fiber space, leading to memory efficiency and enhanced sample efficiency for 3D pick and place tasks. Additionally, the proposed methods demonstrate superior performance compared to baseline approaches in select RLBench tasks.
While the paper demonstrates strong results on RLBench tasks, it's important to note that some tasks like "stack-blocks" and "stack-cups" primarily operate in 2D space, which may not fully reveal the strengths of the methods in 3D. It would be valuable to include additional tasks that involve more 3D rotation angles, such as “put books on bookshelf”.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRobot Manipulation and Learning · Reinforcement Learning in Robotics · Human Pose and Action Recognition