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首次观测 · 记录 #1CVF · CVPR 2026:cvf:89e1e2160048e83db67c19fbf785722dCVF · CVPR 2026

FM-Steer: Enhance Generalist Policies with Value-Guided Cascaded Denoising

Song, Haoming, Qu, Delin, Yao, Yuanqi, Chen, Qizhi, Li, Jiarui, Lv, Qi, Tang, Yiwen, Kang, Li 等 18 位作者

原文摘要

Humans naturally allocate more time before acting when handling complex tasks in the physical world. This paradigm has recently led to remarkable advances in boosting Large Language Models (LLMs) on complex tasks in digital domains. However, the potential of test-time computing remains largely unexplored for robotic foundation models that interact with the physical world. In this work, we propose FM-Steer, a test-time computing framework that augments flow-based Vision-Language-Action (VLA) generalist policies with value-guided sampling and cascaded action denoising, enabling stronger control performance and real-time action rates for dexterous robot manipulation. FM-Steer first introduces an intermediate flow verifier to estimate state-action values for candidate actions. At test time, the policy iteratively samples multiple noisy action proposals and retains the one with the highest predicted value, yielding value-aligned, high-quality actions without retraining. To satisfy the stringent frequency demands of robot control, FM-Steer further introduces cascaded action denoising, decoupling expensive value-guided sampling from fast action refinement. A lightweight Lite-Flow denoiser asynchronously takes the selected high-value noisy action and rapidly denoises it to produce the final control signal, enabling fluid, high-rate execution. During deployment, the intermediate verifier operates at a low frequency to provide value-guided sampling, while the Lite-Flow denoiser continually processes selected candidates to maintain real-time control. Extensive experiments demonstrate that FM-Steer scales flow-based VLA models effectively at test time and achieves state-of-the-art performance across diverse simulation benchmarks and real-world dexterous robotic tasks. The source code is available on the project page: https://hume-vla.github.io.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:9845d8970bef29ee76d53309f718b647CVF · CVPR 2026

ACoT-VLA: Action Chain-of-Thought for Vision-Language-Action Models

Zhong, Linqing, Liu, Yi, Wei, Yifei, Xiong, Ziyu, Liu, Si, Ren, Guanghui

原文摘要

Vision-Language-Action models have emerged as essential generalist robot policies for diverse manipulation tasks, conventionally relying on directly translating multimodal inputs into actions via Vision-Language Model embeddings. Recent advancements have introduced explicit intermediary reasoning--such as sub-task prediction (language) or goal image synthesis (vision)--to guide action generation. However, these intermediate reasoning are often indirect and inherently limited in their capacity to convey the full, granular information required for precise action execution. Instead, we posit that the most effective form of reasoning is one that deliberates directly in the action space. We introduce Action Chain-of-Thought (ACoT), a paradigm where the reasoning process itself is formulated as a structured sequence of coarse action intents that guide the final policy. In this paper, we propose ACoT-VLA, a novel architecture that materializes the ACoT paradigm. Specifically, we introduce two complementary components: an Explicit Action Reasoner (EAR) and Implicit Action Reasoner (IAR). The former proposes coarse reference trajectories as explicit action-level reasoning steps, while the latter extracts latent action priors from internal representations of multimodal input, co-forming an ACoT that conditions the downstream action head to enable grounded policy learning. Extensive experiments in real-world and simulation environments demonstrate the superiority of our proposed method. Code is available at: https://github.com/AgibotTech/ACoT-VLA.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:9870baf60d85873101c1c2e21dfef42bCVF · CVPR 2026

Real2Edit2Real: Generating Robotic Demonstrations via a 3D Control Interface

Zhao, Yujie, Fan, Hongwei, Chen, Di, Chen, Shengcong, Chen, Liliang, Li, Xiaoqi, Ren, Guanghui, Dong, Hao

原文摘要

Recent progress in robot learning has been driven by large-scale datasets and powerful visuomotor policy architectures, yet policy robustness remains limited by the substantial cost of collecting diverse demonstrations, particularly for spatial generalization in manipulation tasks. To reduce repetitive data collection, we present Real2Edit2Real, a framework that generates new demonstrations by bridging 3D editability with 2D visual data through a 3D control interface. Our approach first reconstructs scene geometry from multi-view RGB observations with a metric-scale 3D reconstruction model. Based on the reconstructed geometry, we perform depth-reliable 3D editing on point clouds to generate new manipulation trajectories while geometrically correcting the robot poses to recover physically consistent depth, which serves as a reliable condition for synthesizing new demonstrations. Finally, we propose a multi-conditional video generation model guided by depth as the primary control signal, together with action, edge, and ray maps, to synthesize spatially augmented multi-view manipulation videos. Experiments on four real-world manipulation tasks demonstrate that policies trained on data generated from only 1-5 source demonstrations can match or outperform those trained on 50 real-world demonstrations, improving data efficiency by up to 10-50x. Moreover, experimental results on height and texture editing demonstrate the framework's flexibility and extensibility, indicating its potential to serve as a unified data generation framework.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗