[Paper Review] Diffusion-EDFs: Bi-equivariant Denoising Generative Modeling on SE(3) for Visual Robotic Manipulation

Hyunwoo Ryu, Jiwoo Kim+8 more|arXiv (Cornell University)|Sep 6, 2023

Generative Adversarial Networks and Image SynthesisComputer Science3 citations

TL;DR

This paper introduces Diffusion-EDFs, a novel SE(3)-equivariant diffusion generative model for visual robotic manipulation that achieves end-to-end training from only 5–10 human demonstrations in under an hour. By leveraging bi-equivariance and a hierarchical architecture, it enables data-efficient, generalizable policy learning with superior robustness and real-world deployment capability on unseen object configurations.

ABSTRACT

Diffusion generative modeling has become a promising approach for learning robotic manipulation tasks from stochastic human demonstrations. In this paper, we present Diffusion-EDFs, a novel SE(3)-equivariant diffusion-based approach for visual robotic manipulation tasks. We show that our proposed method achieves remarkable data efficiency, requiring only 5 to 10 human demonstrations for effective end-to-end training in less than an hour. Furthermore, our benchmark experiments demonstrate that our approach has superior generalizability and robustness compared to state-of-the-art methods. Lastly, we validate our methods with real hardware experiments. Project Website: https://sites.google.com/view/diffusion-edfs/home

Motivation & Objective

• To address the limitations of existing diffusion models in robotic manipulation, which require large demonstration sets and lack generalization to out-of-distribution configurations.
• To develop a bi-equivariant diffusion model on SE(3) that preserves rotational and translational symmetry for improved data efficiency and robustness.
• To reduce training time significantly compared to prior energy-based models like EDFs while maintaining end-to-end trainability and generalization.
• To enable scene-level context understanding through a hierarchical architecture that captures long-range dependencies in point cloud observations.
• To validate the method through comprehensive simulation and real-robot experiments on diverse manipulation tasks.

Proposed method

• The method employs a diffusion-based score-based generative model conditioned on 3D point cloud observations, with a learned score function that is bi-equivariant under SE(3) transformations.
• It uses a hierarchical architecture that combines local and global feature representations to capture both fine-grained and scene-level context in point cloud inputs.
• The model is trained via a denoising process where the end-effector pose is gradually corrupted and then reconstructed using a score-based diffusion process on the SE(3) manifold.
• Bi-equivariance is enforced through the use of SO(3)-equivariant message passing layers based on irreducible representations (spin-0 to spin-l), ensuring consistent transformation behavior under rotations and translations.
• A multi-scale denoising head is used for high-resolution pose generation, with separate models trained for pick and place subtasks.
• An energy-based critic is employed post-sampling to rank and select high-quality grasp poses from multiple diffusion samples.

Figure 1 : Overview of Diffusion-EDFs. (a) The target end-effector pose $g_{0}$ is bi-equivariantly diffused for the training of Diffusion-EDFs. (b) The end-effector pose is sampled from the policy by denoising with learned bi-equivariant score function. Due to the bi-equivariance, the trained polic

Experimental results

Research questions

• RQ1 Can a diffusion-based generative model achieve data-efficient, end-to-end learning of 6-DoF robotic manipulation policies from only 5–10 human demonstrations?
• RQ2 How does SE(3)-bi-equivariance in a diffusion model improve generalization to previously unseen object poses and configurations?
• RQ3 Can the proposed method significantly reduce training time compared to prior energy-based models like EDFs while maintaining performance?
• RQ4 To what extent does the hierarchical architecture enhance the model's ability to reason about scene-level context in point cloud observations?
• RQ5 How well does the method generalize to real-world scenarios involving novel object instances, adversarial distractors, and non-standard object orientations?

Key findings

• Diffusion-EDFs achieves effective end-to-end training in less than an hour using only 5 to 10 human demonstrations, representing a 15x speedup over prior EDF methods.
• The method generalizes robustly to out-of-distribution configurations, including previously unseen object poses (e.g., oblique orientations) and novel object instances, with success rates exceeding 90% in human evaluation.
• Real-world experiments on mug-on-hanger, bowls-on-dishes, and bottles-on-shelf tasks confirm the method's ability to generate valid grasp poses from raw point cloud observations with minimal supervision.
• Sampling times range from 5–10 seconds for 20 pick poses and 9–17 seconds for 10 place poses, depending on scene complexity, with longer inference for higher-point-cloud scenes like bowls-on-dishes.
• Training with an RTX 3090 GPU takes less than 24 minutes for mug-picking, less than 36 minutes for mug-placing, 27 minutes for bottle-picking, and up to 1.3 hours for bowl-placing, with parallel training of subtask models reducing total time to under an hour with three GPUs.
• The hierarchical architecture enables the model to distinguish scene-level context, improving performance over object-centric approaches in complex, cluttered environments.

Figure 2 : Architecture of multiscale EDF. Our multiscale EDF model is composed of a feature extracting part and a field model part. See Fig. 7 in Supp. D.3 for details on each module in the architecture. (a) The feature extractor encodes the input point cloud into multiscale featured point clouds.