ReSeFlow: Rectifying SE(3)-Equivariant Policy Learning Flows

• URL: http://arxiv.org/abs/2509.22695v1
• Date: Sat, 20 Sep 2025 06:32:36 GMT
• Title: ReSeFlow: Rectifying SE(3)-Equivariant Policy Learning Flows
• Abstract summary: We introduce the rectification to the SE(3)-diffusion models and propose the ReSeFlow, providing fast, geodesic-consistent, least-computational policy generation.<n>We find that the proposed ReSeFlow with only one inference step can achieve better performance with lower geodesic distance than the baseline methods.
• Score: 7.360373380580255
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robotic manipulation in unstructured environments requires the generation of robust and long-horizon trajectory-level policy with conditions of perceptual observations and benefits from the advantages of SE(3)-equivariant diffusion models that are data-efficient. However, these models suffer from the inference time costs. Inspired by the inference efficiency of rectified flows, we introduce the rectification to the SE(3)-diffusion models and propose the ReSeFlow, i.e., Rectifying SE(3)-Equivariant Policy Learning Flows, providing fast, geodesic-consistent, least-computational policy generation. Crucially, both components employ SE(3)-equivariant networks to preserve rotational and translational symmetry, enabling robust generalization under rigid-body motions. With the verification on the simulated benchmarks, we find that the proposed ReSeFlow with only one inference step can achieve better performance with lower geodesic distance than the baseline methods, achieving up to a 48.5% error reduction on the painting task and a 21.9% reduction on the rotating triangle task compared to the baseline's 100-step inference. This method takes advantages of both SE(3) equivariance and rectified flow and puts it forward for the real-world application of generative policy learning models with the data and inference efficiency.

Related papers

• Secrecy Energy Efficiency for IRS-Assisted Low-Altitude Communications: A D3QN-PER Based Approach [25.752091807817816]
  Security and energy challenges in low-altitude economy (LAE) wireless communications are addressed.<n>We develop a secure synergistic network integrating unmanned aerial vehicle (UAV) and intelligent surface (IRS)<n>We show that the developed D3Q-PERN-based learning approaches outperforms existing state-of-the-art learning approaches.
  arXiv  Detail & Related papers  (2026-07-27T09:05:35Z)
• Self-Consistent Flow: Unifying Velocity and Endpoint Prediction for Rectified Flow Models [7.517070288522858]
  In rectified-flow-based generative models, the neural network can be trained to predict two different targets, such as the instantaneous velocity or the data endpoint.<n>We show that predicting the data endpoint has a clear training signal that stabilizes training, whereas predicting the velocity maintains stable sampling dynamics near the data manifold.<n>Motivated by these insights, we propose Self-Consistent Flow (SC-Flow), a new method that unifies the benefits of both parameterizations.
  arXiv  Detail & Related papers  (2026-07-13T21:31:45Z)
• FAVE: Flow-based Average Velocity Establishment for Sequential Recommendation [33.3202615024807]
  We propose a Flow-based Average Velocity Establishment (Fave) framework for one-step generation recommendation.<n>Fave learns a direct trajectory from an informative prior to the target distribution.<n>Experiments on three benchmarks demonstrate that Fave not only achieves state-of-the-art recommendation performance but also delivers an order-of-magnitude improvement in inference efficiency.
  arXiv  Detail & Related papers  (2026-04-06T05:17:43Z)
• An Adapter-free Fine-tuning Approach for Tuning 3D Foundation Models [22.63554774942836]
  We propose Momentum-Consistency Fine-Tuning (MCFT) to bridge the gap between full and parameter-efficient fine-tuning.<n>MCFT selectively fine-tunes a portion of the pre-trained encoder while enforcing a momentum-based consistency constraint.<n>We extend MCFT with two variants: a semi-supervised framework that leverages abundant unlabeled data to enhance few-shot performance, and a pruning-based variant that improves computational efficiency.
  arXiv  Detail & Related papers  (2026-03-24T21:28:37Z)
• Efficient Hybrid SE(3)-Equivariant Visuomotor Flow Policy via Spherical Harmonics for Robot Manipulation [51.48086451600577]
  E3Flow is a novel framework that addresses the limitations of equivariant diffusion policies.<n>It unifies efficient rectified flow with stable, multi-modal equivariant learning for the first time.<n>E3Flow achieves a 3.12% improvement in average success rate over the state-of-the-art Spherical Diffusion Policy.
  arXiv  Detail & Related papers  (2026-03-24T14:00:36Z)
• Test-Time Adaptation for Anomaly Segmentation via Topology-Aware Optimal Transport Chaining [10.091031517157411]
  TopoOT is a topology-aware optimal transport (OT) framework.<n>It integrates multi-filtration persistence diagrams (PDs) with test-time adaptation (TTA)<n>TopoOT achieves state-of-the-art performance across 2D and 3D anomaly detection benchmarks.
  arXiv  Detail & Related papers  (2026-01-28T07:49:28Z)
• Beyond Confidence: Adaptive and Coherent Decoding for Diffusion Language Models [64.92045568376705]
  Coherent Contextual Decoding (CCD) is a novel inference framework built upon two core innovations.<n>CCD employs a trajectory rectification mechanism that leverages historical context to enhance sequence coherence.<n>Instead of rigid allocations based on diffusion steps, we introduce an adaptive sampling strategy that dynamically adjusts the unmasking budget for each step.
  arXiv  Detail & Related papers  (2025-11-26T09:49:48Z)
• Iterative Refinement of Flow Policies in Probability Space for Online Reinforcement Learning [56.47948583452555]
  We introduce the Stepwise Flow Policy (SWFP) framework, founded on the key insight that discretizing the flow matching inference process via a fixed-step Euler scheme aligns it with the variational Jordan-Kinderlehrer-Otto principle from optimal transport.<n>SWFP decomposes the global flow into a sequence of small, incremental transformations between proximate distributions.<n>This decomposition yields an efficient algorithm that fine-tunes pre-trained flows via a cascade of small flow blocks, offering significant advantages.
  arXiv  Detail & Related papers  (2025-10-17T07:43:51Z)
• Transformer-based Scalable Beamforming Optimization via Deep Residual Learning [12.79709425087431]
  unsupervised deep learning framework for downlink beamforming in large-scale MU-MISO channels.<n>Model is trained offline, allowing real-time inference through lightweight feedforward computations in dynamic communication environments.
  arXiv  Detail & Related papers  (2025-10-15T01:43:51Z)
• TOAST: Task-Oriented Adaptive Semantic Transmission over Dynamic Wireless Environments [3.3107717550009865]
  TOAST (Task-Oriented Adaptive Semantic Transmission) is a unified framework designed to address the core challenge of multi-task optimization in wireless environments.<n>We formulate adaptive task balancing as a Markov decision process, employing deep reinforcement learning to dynamically adjust the trade-off between image reconstruction fidelity and semantic classification accuracy.<n>We integrate module-specific Low-Rank Adaptation (LoRA) mechanisms throughout our Swin Transformer-based joint source-channel coding architecture.
  arXiv  Detail & Related papers  (2025-06-27T04:36:30Z)
• Flow-GRPO: Training Flow Matching Models via Online RL [75.70017261794422]
  We propose Flow-GRPO, the first method integrating online reinforcement learning (RL) into flow matching models.<n>Our approach uses two key strategies: (1) an ODE-to-SDE conversion that transforms a deterministic Ordinary Equation (ODE) into an equivalent Differential Equation (SDE) that matches the original model's marginal distribution at all timesteps; and (2) a Denoising Reduction strategy that reduces training denoising steps while retaining the original inference timestep number.
  arXiv  Detail & Related papers  (2025-05-08T17:58:45Z)
• FlowTS: Time Series Generation via Rectified Flow [67.41208519939626]
  FlowTS is an ODE-based model that leverages rectified flow with straight-line transport in probability space.<n>For unconditional setting, FlowTS achieves state-of-the-art performance, with context FID scores of 0.019 and 0.011 on Stock and ETTh datasets.<n>For conditional setting, we have achieved superior performance in solar forecasting.
  arXiv  Detail & Related papers  (2024-11-12T03:03:23Z)
• 3D Equivariant Pose Regression via Direct Wigner-D Harmonics Prediction [50.07071392673984]
  Existing methods learn 3D rotations parametrized in the spatial domain using angles or quaternions. We propose a frequency-domain approach that directly predicts Wigner-D coefficients for 3D rotation regression. Our method achieves state-of-the-art results on benchmarks such as ModelNet10-SO(3) and PASCAL3D+.
  arXiv  Detail & Related papers  (2024-11-01T12:50:38Z)
• Adaptive Anomaly Detection in Network Flows with Low-Rank Tensor Decompositions and Deep Unrolling [9.20186865054847]
  Anomaly detection (AD) is increasingly recognized as a key component for ensuring the resilience of future communication systems.<n>This work considers AD in network flows using incomplete measurements.<n>We propose a novel block-successive convex approximation algorithm based on a regularized model-fitting objective.<n>Inspired by Bayesian approaches, we extend the model architecture to perform online adaptation to per-flow and per-time-step statistics.
  arXiv  Detail & Related papers  (2024-09-17T19:59:57Z)
• Rational-WENO: A lightweight, physically-consistent three-point weighted essentially non-oscillatory scheme [14.120671138290104]
  We employ a rational neural network to accurately estimate the local smoothness of the solution. This approach achieves a granular reconstruction with significantly reduced dissipation. We demonstrate the effectiveness of our approach on several one-, two-, and three-dimensional fluid flow problems.
  arXiv  Detail & Related papers  (2024-09-13T22:11:03Z)
• SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation [66.16525145765604]
  We introduce an SE(3) diffusion model-based point cloud registration framework for 6D object pose estimation in real-world scenarios. Our approach formulates the 3D registration task as a denoising diffusion process, which progressively refines the pose of the source point cloud. Experiments demonstrate that our diffusion registration framework presents outstanding pose estimation performance on the real-world TUD-L, LINEMOD, and Occluded-LINEMOD datasets.
  arXiv  Detail & Related papers  (2023-10-26T12:47:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.