KAN We Flow? Advancing Robotic Manipulation with 3D Flow Matching via KAN & RWKV

• URL: http://arxiv.org/abs/2602.01115v1
• Date: Sun, 01 Feb 2026 09:27:56 GMT
• Title: KAN We Flow? Advancing Robotic Manipulation with 3D Flow Matching via KAN & RWKV
• Abstract summary: KAN-We-Flow is a flow-matching policy that builds a lightweight backbone for 3D manipulation.<n>Our design reduces parameters by 86.8%, maintains fast runtime, and achieves state-of-the-art success rates on Adroit, Meta-World, and DexArt benchmarks.
• Score: 15.840121055564405
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion-based visuomotor policies excel at modeling action distributions but are inference-inefficient, since recursively denoising from noise to policy requires many steps and heavy UNet backbones, which hinders deployment on resource-constrained robots. Flow matching alleviates the sampling burden by learning a one-step vector field, yet prior implementations still inherit large UNet-style architectures. In this work, we present KAN-We-Flow, a flow-matching policy that draws on recent advances in Receptance Weighted Key Value (RWKV) and Kolmogorov-Arnold Networks (KAN) from vision to build a lightweight and highly expressive backbone for 3D manipulation. Concretely, we introduce an RWKV-KAN block: an RWKV first performs efficient time/channel mixing to propagate task context, and a subsequent GroupKAN layer applies learnable spline-based, groupwise functional mappings to perform feature-wise nonlinear calibration of the action mapping on RWKV outputs. Moreover, we introduce an Action Consistency Regularization (ACR), a lightweight auxiliary loss that enforces alignment between predicted action trajectories and expert demonstrations via Euler extrapolation, providing additional supervision to stabilize training and improve policy precision. Without resorting to large UNets, our design reduces parameters by 86.8\%, maintains fast runtime, and achieves state-of-the-art success rates on Adroit, Meta-World, and DexArt benchmarks. Our project page can be viewed in \href{https://zhihaochen-2003.github.io/KAN-We-Flow.github.io/}{\textcolor{red}{link}}

Related papers

• StructRL: Structured Action-Space Exploration for Flow-Based VLAs [81.43458629514139]
  Flow-based Vision-Language-Action models are now widely used for continuous robotic manipulation.<n>Online reinforcement learning is emerging as a key technique for adapting them to new tasks.<n>We show that effective robot exploration calls for structured noise: temporally smooth and scaled differently across action groups.
  arXiv  Detail & Related papers  (2026-08-15T09:31:29Z)
• Flowing With Purpose: Latent Action Guided Flow Matching Policies For Robotic Manipulation [41.66477383019748]
  State-of-the-art flow matching policies rely on a globally fixed isotropic source distribution.<n>Latent Action Guided Flow Matching replaces the monolithic Gaussian with an adaptive library of learned prior distributions.<n>LAFM substantially outperforms standard flow matching formulations.
  arXiv  Detail & Related papers  (2026-06-22T14:42:59Z)
• ReFPO: Reflow Regularization for Flow Matching Policy Gradients [58.32178725687043]
  We present a simple online RL method that adds explicit Reflow regularization to Flow Matching Policy Gradients.<n>We experimentally demonstrate that ReFPO improves average performance and discretization across GridWorld, MuJoCo Playground, and high-dimensional Humanoid Control tasks.
  arXiv  Detail & Related papers  (2026-06-19T04:23:10Z)
• Flow-DPPO: Divergence Proximal Policy Optimization for Flow Matching Models [61.74572554094633]
  We argue that ratio clipping is structurally ill-suited for flow models.<n>We propose Flow-DPPO, which replaces ratio clipping with a divergence proximal constraint.<n> Experiments show that Flow-DPPO achieves higher rewards with better KL-proximal efficiency.
  arXiv  Detail & Related papers  (2026-06-09T15:59:57Z)
• Efficient RWKV-based Representation Learning for 3D Point Clouds [53.911842457391636]
  The textbfP-RWKV block bridges the gap between sequence modeling and irregular 3D geometry.<n>The P-RWKV block and its key sub-modules achieve competitive performance across various tasks with lower computational cost and inference latency.
  arXiv  Detail & Related papers  (2026-06-09T04:16:39Z)
• FlowPRO: Reward-Free Reinforced Fine-Tuning of Flow-Matching VLAs via Proximalized Preference Optimization [15.427278126226662]
  FlowPRO is a reward-free, offline reinforced fine-tuning framework for flow-matching policies.<n>We present RPRO, a preference-optimization objective tailored to the flow-matching action head of VLA models.<n>FlowPRO attains the highest success rate, outperforming four representative baselines.
  arXiv  Detail & Related papers  (2026-06-03T21:47:43Z)
• Unbiased Gradient Estimation for Event Binning via Functional Backpropagation [64.88399635309918]
  We propose a novel framework for unbiased gradient estimation of arbitrary binning functions by synthesizing weak derivatives during backpropagation.<n>We achieve 9.4% lower EPE in self-supervised optical flow, and 5.1% lower RMS error in SLAM, demonstrating broad benefits for event-based visual perception.
  arXiv  Detail & Related papers  (2026-02-13T04:05:03Z)
• From GNNs to Symbolic Surrogates via Kolmogorov-Arnold Networks for Delay Prediction [3.571534406261392]
  We implement a heterogeneous GNN with attention-based message passing, establishing a strong neural baseline.<n>Second, we propose FlowKANet in which Kolmogorov-Arnold Networks replace standard layers, reducing trainable parameters.<n>Third, we distill the model into symbolic surrogate models using block-wise regression, producing closed-form equations that eliminate trainable computation.
  arXiv  Detail & Related papers  (2025-12-24T02:05:46Z)
• Steering Vision-Language-Action Models as Anti-Exploration: A Test-Time Scaling Approach [78.4812458793128]
  We propose textbfTACO, a test-time-scaling framework that applies a lightweight pseudo-count estimator as a high-fidelity verifier of action chunks.<n>Our method resembles the classical anti-exploration principle in offline reinforcement learning (RL), and being gradient-free, it incurs significant computational benefits.
  arXiv  Detail & Related papers  (2025-12-02T14:42:54Z)
• 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)
• Continual Action Quality Assessment via Adaptive Manifold-Aligned Graph Regularization [53.82400605816587]
  Action Quality Assessment (AQA) quantifies human actions in videos, supporting applications in sports scoring, rehabilitation, and skill evaluation.<n>A major challenge lies in the non-stationary nature of quality distributions in real-world scenarios.<n>We introduce Continual AQA (CAQA), which equips with Continual Learning capabilities to handle evolving distributions.
  arXiv  Detail & Related papers  (2025-10-08T10:09:47Z)
• SAC Flow: Sample-Efficient Reinforcement Learning of Flow-Based Policies via Velocity-Reparameterized Sequential Modeling [9.936731043466699]
  Training expressive flow-based policies with off-policy reinforcement learning is notoriously unstable due to gradient pathologies in the multi-step action sampling process.<n>We trace this instability to a fundamental connection: the flow rollout is algebraically equivalent to a residual recurrent computation, making it susceptible to the same vanishing and exploding gradients as RNNs.<n>We develop a practical SAC-based algorithm, enabled by a noise-augmented rollout, that facilitates direct end-to-end training of these policies.
  arXiv  Detail & Related papers  (2025-09-30T04:21:20Z)
• ReinFlow: Fine-tuning Flow Matching Policy with Online Reinforcement Learning [7.452631636467998]
  ReinFlow is an online reinforcement learning framework for continuous robotic control.<n>It injects learnable noise into a flow policy's deterministic path, converting the flow into a discrete-time Markov Process.<n>We benchmark ReinFlow in representative locomotion and manipulation tasks.
  arXiv  Detail & Related papers  (2025-05-28T08:17:16Z)
• Flow-GRPO: Training Flow Matching Models via Online RL [80.62659379624867]
  We propose Flow-GRPO, the first method to integrate online policy reinforcement learning into flow matching models.<n>Our approach uses two key strategies: (1) an ODE-to-SDE conversion that transforms a deterministic Ordinary Differential Equation 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 number of inference steps.
  arXiv  Detail & Related papers  (2025-05-08T17:58:45Z)
• ALOcc: Adaptive Lifting-Based 3D Semantic Occupancy and Cost Volume-Based Flow Predictions [91.55655961014027]
  3D semantic occupancy and flow prediction are fundamental to understanding scene scene.<n>This paper proposes a vision-based framework with three targeted improvements.<n>Our purely convolutional architecture establishes new SOTA performance on multiple benchmarks for both semantic occupancy and joint semantic-flow prediction.
  arXiv  Detail & Related papers  (2024-11-12T11:32:56Z)
• PointRWKV: Efficient RWKV-Like Model for Hierarchical Point Cloud Learning [56.14518823931901]
  We present PointRWKV, a model of linear complexity derived from the RWKV model in the NLP field. We first propose to explore the global processing capabilities within PointRWKV blocks using modified multi-headed matrix-valued states. To extract local geometric features simultaneously, we design a parallel branch to encode the point cloud efficiently in a fixed radius near-neighbors graph with a graph stabilizer.
  arXiv  Detail & Related papers  (2024-05-24T05:02:51Z)
• Reachable Polyhedral Marching (RPM): An Exact Analysis Tool for Deep-Learned Control Systems [11.93664682521114]
  We focus our attention on feedforward neural networks with the rectified unit (ReLU) activation.<n>We provide an accelerated algorithm for computing ROAs that leverages the incremental and connected of affine regions.<n>Finally, we apply our methods to find a set of states that are stabilized by an image-based controller for an aircraft runway control problem.
  arXiv  Detail & Related papers  (2022-10-15T17:15:53Z)
• GMFlow: Learning Optical Flow via Global Matching [124.57850500778277]
  We propose a GMFlow framework for learning optical flow estimation. It consists of three main components: a customized Transformer for feature enhancement, a correlation and softmax layer for global feature matching, and a self-attention layer for flow propagation. Our new framework outperforms 32-iteration RAFT's performance on the challenging Sintel benchmark.
  arXiv  Detail & Related papers  (2021-11-26T18:59:56Z)

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.