2026-06-13

Improving Robotic Generalist Policies via Flow Reversal Steering

第一作者: Andy Tang · 方向: 机器人操作 · 来源: cs.RO

06/12 01:59

机器人操作通用策略流匹配流反转引导零样本控制隐藏英文摘要

Generalist policies can learn a wide range of skills from diverse robot datasets. In order to solve or improve on challenging news tasks, we need a way to infer and invoke the appropriate actions from the policy's rich behavioral prior, especially when directly commanding the policy fails. We focus on flow matching generalists and propose Flow Reversal Steering (FRS): a method that takes suboptimal but ``reasonable'' actions, finds their latent noises by passing them through the flow policy in reverse, and maps them to nearby generalist action modes. We evaluate FRS across many simulated and real-world manipulation settings. First, FRS can turn coarse semantic guidance from humans or vision-language models (VLMs) into corresponding good robot actions, improving zero-shot control. These gains can be distilled with behavioral cloning by training an auxiliary policy to output noises that...

论文介绍 通用机器人策略虽能从多样数据中学习技能，但在面对新任务或直接指令失败时，难以有效调用其知识库。本文聚焦于基于流匹配的通用策略，提出了「流反转引导」方法。其核心思想是：将粗略但合理的动作通过策略逆向推导出潜在噪声，再将其映射到通用策略的高质量动作模式附近。该方法能将来自人类或视觉语言模型的粗糙语义引导转化为精确的机器人动作，在模拟和真实操作任务中提升了零样本控制性能。

NavWAM: A Navigation World Action Model for Goal-Conditioned Visual Navigation

第一作者: Daichi Azuma · 方向: 导航与运动 · 来源: cs.RO

06/11 23:44

目标导航视觉导航世界模型扩散变换器预测控制隐藏英文摘要

Goal-conditioned visual navigation requires a robot to act under partial observability by anticipating how its motion will change the future egocentric view and whether that change brings it closer to the goal. Navigation world models provide such visual foresight, but they remain prediction modules that require an external planner to convert predicted futures into closed-loop control. We propose Navigation World Action Model (NavWAM), a diffusion-transformer policy that turns navigation world-model prediction into executable action by representing future observations, goal-progress values, and action chunks in a shared latent sequence. By learning future prediction jointly with the action and value targets that determine closed-loop behavior, NavWAM makes visual foresight directly usable for robot control. We build NavWAM through simulation pretraining and real-robot adaptation, and...

论文介绍 目标导向视觉导航需要机器人在部分可观测下进行动作规划，这要求对未来视觉变化有预见能力。传统导航世界模型是独立的预测模块，需额外规划器将其转化为控制指令。论文提出了「导航世界动作模型」，这是一个扩散变换器策略。它将未来观测、目标进度值和动作序列统一建模在共享的潜在空间中，使对未来状态的视觉预见能力直接服务于闭环控制决策，简化了从预测到行动的流程。

See Selectively, Act Adaptively: Dual-Level Structural Decomposition for Bimanual Robot Manipulation

第一作者: Yoon-Ji Choi · 方向: VLA 通用模型 · 来源: cs.RO

06/11 20:33

双臂操作视觉语言动作模型结构分解视觉选择交互感知隐藏英文摘要

In bimanual robotic manipulation, task-relevant visual information varies with the task stage and context, while the interaction of the two arms shifts between independent and coordinated modes, making policy learning challenging. However, existing monolithic Vision-Language-Action (VLA) policies process diverse visual inputs and interaction patterns through a single shared representation and action generation pathway, often failing to separately account for visual relevance and bimanual interaction structure. To address this issue, we propose a bimanual manipulation VLA framework based on Dual-Level Structural Decomposition. The View-Selective Visual Router dynamically adjusts wrist-view contributions to emphasize relevant visual cues, while the Interaction-Aware Action Mixture-of-Experts (MoE) decomposes action generation into coordinated and arm-wise pathways to adapt to varying...

论文介绍 双臂机器人操作任务中，视觉信息的相关性及双臂交互模式会动态变化，给策略学习带来挑战。现有整体式视觉语言动作模型常采用单一通路处理，难以适应这种变化。为此，论文提出一种基于双层结构分解的框架。其「视觉选择路由器」动态调整各视角的贡献以聚焦关键视觉线索；「交互感知动作混合专家」则将动作生成分解为协调模式和独立臂模式，从而自适应于任务不同阶段的视觉和交互需求。

EA-WM: Event-Aware World Models with Task-Specification Grounding for Long-Horizon Manipulation

第一作者: Kailin Wang · 方向: 机器人操作 · 来源: cs.RO

06/11 16:35

事件感知世界模型任务规范接地长程操作视觉特征预测进度评估隐藏英文摘要

Pretrained-feature world models provide a useful substrate for robot imagination, but visual or latent prediction alone does not determine whether an imagined future satisfies task-relevant events. Long-horizon manipulation requires progress signals that are relational, predicate-level, and physically grounded: whether an object has moved, whether a drawer or contact state has changed, whether a placement predicate is satisfied, and whether a candidate future is reliable enough for execution. We introduce EA-WM, an event-aware world-model framework that augments frozen visual-feature dynamics with task-specification-grounded event prediction and verification. EA-WM rolls out candidate futures in pretrained visual-feature space, decodes them into structured event states, and scores them using task-progress, semantic-consistency, physical-feasibility, and uncertainty terms. The verifier...

论文介绍 预训练的世界模型缺乏判断想象未来是否满足任务相关事件的能力。针对长程操作对关系性、谓词级进展信号的需求，本文提出EA-WM框架。该框架在预训练视觉特征空间中预测未来状态，将其解码为结构化事件状态，并基于任务进度、语义一致性、物理可行性和不确定性进行评分验证，从而增强机器人长程操作的规划与执行可靠性。

GeoCFNet: Geometry-Aware Confidence Field Network for Robot-Assisted Endoscopic Submucosal Dissection

第一作者: Rui Tang · 方向: 具身智能 · 来源: cs.CV

06/11 16:06

内镜粘膜剥离术置信场预测几何感知网络解剖引导组织边缘安全隐藏英文摘要

Advanced surgical robotics has made robot-assisted endoscopic submucosal dissection (ESD) a promising approach for the en-bloc resection of large lesions, with the potential to reduce recurrence and improve long-term outcomes. However, the technical complexity and risk of complications in ESD demand stable and precise visual guidance to maintain an accurate dissection corridor and a safe tissue margin. Dense confidence fields provide an effective representation for this purpose by describing both the preferred dissection region and its spatial transition to surrounding tissue. However, reliable confidence field estimation remains challenging in dynamic endoscopic scenes due to smoke, specular highlights, tissue deformation, weak texture, and the thin geometric structure of the target region. To address these challenges, we formulate dissection guidance as a geometry-aware confidence...

论文介绍 机器人辅助内镜粘膜剥离术需要稳定精确的视觉引导以维持安全的解剖边界。然而，内镜场景中动态干扰（如烟雾、组织变形）使得可靠置信场估计困难。本文将解剖引导问题形式化为几何感知的置信场预测任务，并提出GeoCFNet网络。该方法旨在准确描述首选解剖区域及其向周围组织的空间过渡，为手术机器人提供更安全的引导信号。

Learning to Assist: Collaborative VLAs for Implicit Human-Robot Collaboration

第一作者: Leo Xu · 方向: VLA 通用模型 · 来源: cs.RO

06/10 13:42

隐式人机协作视觉-语言-动作模型模仿学习动作分块策略演示泄漏隐藏英文摘要

Human-robot collaboration (HRC) combines the complementary strengths of humans and robots to improve task efficiency. However, many existing collaborative systems rely on hand-engineered pipelines, limiting their scalability and flexibility for new tasks. In this work, we show that models trained end-to-end with imitation learning, specifically vision-language-action (VLA) models, can support collaborative manipulation, and characterize the key factors affecting their real-world performance. We evaluate two state-of-the-art models and identify a failure mode of action-chunking policies in implicit HRC, where demonstration action leakage (i.e., action chunks crossing latent task transitions) can cause premature assistive behavior. We find that this issue increases with longer execution horizons and occurs in real-world collaborative VLA systems, such as when a robot attempts to hand...

论文介绍 现有人机协作系统多依赖于人工设计流程，限制了其可扩展性。本文研究了使用端到端模仿学习训练的视觉-语言-动作模型来支持隐式协作操作。研究发现，动作分块策略在隐式协作中存在一种“演示泄漏”失败模式，即动作块跨越了潜在的任务阶段边界，导致机器人过早执行辅助行为，该问题在执行时序较长时尤为明显。

Mana: Dexterous Manipulation of Articulated Tools

第一作者: Zhao-Heng Yin · 方向: 机器人操作 · 来源: cs.RO

关节工具操纵灵巧操作模拟到真实迁移关键帧动画强化学习隐藏英文摘要

Abstract:Articulated tool manipulation remains a major challenge in dexterous robotics due to the need to coordinate internal degrees of freedom and contact-rich interactions. While prior work has largely focused on rigid objects, articulated tool use remains underexplored because of its physical complexity and the difficulty of learning functional grasping and manipulation policies. We present Mana (Manipulation Animator), a general sim-to-real framework that reinterprets dexterous manipulation as an animation problem. Inspired by computer animation, Mana employs a coarse-to-fine pipeline that transforms procedurally-generated grasp keyframes into manipulation trajectories through motion planning and reinforcement learning. The data generation process is largely automatic, requiring only a few mouse clicks to specify functional affordances (<1 minute per tool). Across four articulated...

论文介绍 关节工具的灵巧操纵是机器人领域的难题，因其涉及协调内部自由度与复杂的接触交互。本文提出Mana框架，将灵巧操纵重新诠释为动画生成问题。该框架采用从粗到细的流程：首先通过程序化生成抓取关键帧，随后利用运动规划和强化学习将其转化为操纵轨迹。整个过程高度自动化，仅需少量交互即可为新工具定义功能 affordance。

$\texttt{WEAVER}$, Better, Faster, Longer: An Effective World Model for Robotic Manipulation

第一作者: Arnav Kumar Jain · 方向: 机器人操作 · 来源: cs.RO

机器人操纵世界模型多视图预测保真度与一致性强化学习策略改进模拟轨迹隐藏英文摘要

Abstract:The potential impacts of world models (WMs, i.e., learned simulators) on robotics are far-reaching -- policy evaluation, policy improvement, and test-time planning -- all with limited real-world interaction. To unlock these downstream capabilities, a WM needs to jointly satisfy three desiderata: $\textit{(i)}$ fidelity (i.e., producing simulated trajectories that correlate with reality), $\textit{(ii)}$ consistency (i.e., producing simulated trajectories that are coherent over long horizons), and $\textit{(iii)}$ efficiency (i.e., producing simulated trajectories quickly). We propose $\texttt{WEAVER}$ (World Estimation Across Views for Embodied Reasoning): a WM architecture that simultaneously achieves all three desiderata, providing state-of-the-art results on robotic manipulation tasks. $\texttt{WEAVER}$ is a multi-view WM trained to predict future latents and reward values...

论文介绍 高质量的世界模型对于机器人的策略评估、改进和测试时规划至关重要，它需要同时满足高保真、长程一致和高效推理三个要求。本文提出WEAVER，一种多视图世界模型架构。该模型在预训练的视觉-语言特征空间中训练，能够联合预测未来的潜在状态和奖励值，从而在机器人操纵任务中实现了优异的性能平衡。

MCR-Bionic Hand: Anatomical Structural Priors for Dexterous Manipulation

第一作者: Haosen Yang · 方向: 机器人操作 · 来源: cs.RO

仿生手结构先验肌腱骨骼模拟抓取配置关节协调具身智能隐藏英文摘要

Abstract:Dexterous robotic hands are usually formulated as high dimensional active control systems governed by degrees of freedom, actuation, and algorithms. Human hand dexterity, however, is partly encoded in the physical architecture of bones, ligaments, tendons, aponeuroses, and intrinsic muscles. This work describes that contribution as two linked forms of structural intelligence: structural prior generation, in which wrist to finger tenodesis, FDS/FDP routing, and the dorsal extensor hood transform low dimensional posture inputs into default grasp configurations and PIP to DIP coordination; and muscle mediated modulation, in which extrinsic muscles, lumbricals, and interossei regulate MCP posture, distal stability, fingertip force paths, and contact states around that default state. Based on this framework, MCR-Bionic Hand is developed as a 1:1 musculoskeletal biomimetic hand...

论文介绍 人类手部的灵巧性部分源于其骨骼、韧带、肌腱等物理结构所编码的“结构智能”。本文将这种贡献描述为两种形式：结构先验生成和肌肉介导调节。基于此框架，作者开发了MCR-Bionic Hand，这是一款1:1肌肉骨骼仿生手。该设计通过模拟人体手部的解剖结构先验，使低维输入能够自然转化为协调的抓取配置与稳定的操作状态。

SPARC: Reliable Spatial Annotations from Robot Demonstrations at Scale

第一作者: Nils Blank · 方向: 机器人操作 · 来源: cs.RO

机器人演示标注空间注释可靠性风险感知标注训练数据质量任务组合隐藏英文摘要

Abstract:This work introduces Spatial Annotations from Robot Demonstrations with Reliability Calibration (SPARC), a risk-aware framework that automatically labels robot demonstrations with structured spatial annotations and assigns each annotation a reliability score. Structured spatial annotations, such as bounding boxes, object trajectories, and manipulation phase labels, benefit a broad range of robotics applications from training grounded robot policies and embodied foundation models to motion planning and hierarchical task composition. Existing automated pipelines generate such annotations at scale but provide no reliable quality signal: detector confidence is poorly calibrated for annotation correctness, forcing a choice between accepting noisy labels or discarding useful samples. In contrast to existing automated pipelines, SPARC leverages the spatio-temporal structure inherent...

论文介绍 自动化的机器人演示标注对于训练具身基础模型和策略至关重要，但现有流程无法提供可靠的标注质量信号。本文提出SPARC，一个风险感知的自动标注框架。它能够自动为演示数据生成边界框、物体轨迹等结构化空间注释，并为每个注释分配一个校准过的可靠性分数，从而在保持大规模标注能力的同时，帮助用户筛选高质量样本。

GIVE: Grounding Human Gestures in Vision-Language-Action Models

第一作者: Pengfei Liu · 方向: VLA 通用模型 · 来源: cs.RO

手势意图理解视觉-语言-动作模型人机交互意图接地多模态指令隐藏英文摘要

Abstract:Human communication is inherently multimodal, where language is often accompanied by non-verbal cues such as gestures to convey intentions. However, current Vision-Language-Action (VLA) models treat robotic manipulation as a pure text-driven task, overlooking the important role of gestures in Human-Robot Interaction (HRI). This often leads to inaccurate intent grounding and unreliable manipulation when language instructions are ambiguous or underspecified. To address this challenge, we propose GIVE (Gesture Intent via Visual-Semantic Enhancement), an effective approach that enhances pre-trained VLA models with human gesture understanding without architectural modifications. Specifically, GIVE incorporates gesture information through two complementary pathways: a visual pathway that overlays hand skeletons and fingertip rays onto robot observations for explicit object...

论文介绍 当前视觉-语言-动作模型主要处理文本指令，忽略了手势等非语言线索在人机交互中的作用，这在语言指令模糊时会导致操作不可靠。本文提出GIVE方法，通过两条互补路径增强预训练VLA模型的手势理解能力：一条视觉路径将手部骨架和指尖射线叠加到观察图像上，一条语义路径则将手势信息注入语言指令嵌入，从而实现更准确的人类意图接地。

GeoHAT: Geometry-Adaptive Hybrid Action Transformer for Mobile Manipulation

第一作者: Xiangyu Zhu · 方向: 机器人操作 · 来源: cs.RO

移动操作几何感知扩散模型动作生成混合动作转换器隐藏英文摘要

Abstract:Whole-body mobile manipulation requires coordinating mobile base and manipulator under shifting viewpoints, posing challenges in geometric perception and action generation. Current policies either rely on 2D features or sparse 3D representations that lack dense spatial structure, and typically encode arm and base within one action vector that ignores their distinct control demands. Moreover, existing dense fusion strategies risk corrupting pretrained representations under noisy depth while incurring heavy computational overhead. We present GeoHAT, an end-to-end diffusion-based framework built on a simple principle: geometry should be injected only where reliable and attended to only where needed. GeoHAT employs a lightweight Fourier spatial encoder that maps dense per-pixel 3D coordinates into geometric tokens without an additional 3D vision backbone. These tokens are then...

论文介绍 研究全身移动操作中几何感知和动作生成的挑战，提出GeoHAT框架，基于扩散模型，采用轻量傅里叶空间编码器处理密集3D坐标，实现几何自适应混合动作转换，提升机器人操作性能。

Real-Time Execution with Autoregressive Policies

第一作者: Sangkyu Lee · 方向: VLA 通用模型 · 来源: cs.RO

实时执行自回归策略视觉-语言-动作模型分词约束解码隐藏英文摘要

Abstract:Real-time execution, enabled by asynchronous inference that ensures both smooth action trajectories and fast reactivity, is critical for realistic deployments of large-scale Vision-Language-Action models. However, recent work on real-time execution primarily focuses on variants of diffusion policies, even though it is more critical for autoregressive policies given their slower rollout speed in synchronous inference. In contrast, we demonstrate that autoregressive policies can achieve real-time execution by adjusting the tokenization horizon and applying constrained decoding, thereby guaranteeing strict latency bounds that enable multi-trajectory decoding to maximize performance. Across simulated and real-world environments, we find that the autoregressive policy consistently outperforms its equivalent-level flow-matching policy counterpart while achieving significantly...

论文介绍 针对大规模视觉-语言-动作模型的实时执行问题，研究自回归策略的优化方法，通过调整分词范围和约束解码实现异步推理，保证严格延迟界限，在模拟和真实环境中提升性能。

Low cost, easily manufactured, highly flexible strain and touch sensitive fiber for robotics applications

第一作者: Christian Diaz Herrera · 方向: 机器人操作 · 来源: cs.RO

低成本传感器导电纤维应变传感器触摸传感器机器人应用隐藏英文摘要

Abstract:Existing stretch and touch sensors for robots are generally expensive with respect to at least one of material costs, required manufacturing equipment, or manufacturing time. We present and experimentally characterize a conductive fiber made using only inexpensive commercial off-the-shelf parts (conductive thread at $0.07/ft, silicone tubing at $0.94/ft) and tools (loop-style needle threader at $2), which can be manufactured quickly (20 cm length in 2 minutes.) We demonstrate its use as a resistive strain sensor with three applications: Triggering a grasp in a pneumatically actuated assistive finger, sensing the pose of a pneumatically actuated robotic strap, and estimating the pose of a flexible solid. We also demonstrate that it can be used as a capacitive sensor with two applications: First, as a touch sensor which triggers a commercial robot arm to move, and second, as a...

论文介绍 针对机器人传感器成本高的问题，提出低成本、易制造的导电纤维，使用廉价材料快速制造，兼具电阻应变和电容触摸感知功能，演示了在辅助设备和机器人操作中的应用。

EMG-Based Adaptation of Anisotropic Virtual Fixtures for Robot-Assisted Surgical Resection and Dissection

第一作者: Dario Onfiani · 方向: 具身智能 · 来源: cs.RO

EMG信号虚拟夹具机器人辅助手术自适应控制腹腔镜手术隐藏英文摘要

Abstract:In this paper, we address the development of an adaptive assistance system for robot-assisted laparoscopic surgery, specifically for delicate tasks such as Resection and Dissection. Even if Virtual Fixtures offer significant advantages for guiding a surgeon's movements, conventional Virtual Fixtures are often defined by fixed geometries, lacking the flexibility to adapt to the surgical workflow or the surgeon's immediate intent. To address these limitations, we propose a novel framework for an adaptive and anisotropic virtual fixture. In addition, we introduce an intuitive control interface that modulates the fixture's geometry in real-time based on the surgeon's intent, inferred from EMG signals. This approach allows the surgeon to dynamically expand or disengage the constraint by contracting their forearm muscles, enabling seamless transitions between precise guided motion...

论文介绍 针对机器人辅助腹腔镜手术中虚拟夹具的固定性限制，提出基于肌电信号的自适应虚拟夹具框架，允许外科医生实时调整夹具几何，实现精确引导和灵活切换，提升手术辅助效果。