2026-06-14

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

机器人通用策略流逆转引导零样本控制流匹配行为克隆隐藏英文摘要

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 本文提出了流逆转引导方法，以提升机器人通用策略在新任务上的表现。该方法对流匹配策略进行逆转，将次优但合理的动作映射回潜在噪声空间，再引导至通用策略的动作模式附近。实验表明，FRS能将来自人类或视觉语言模型的粗粒度语义指导转化为精细的机器人动作，提升零样本控制性能，并可通过行为克隆进行蒸馏学习。

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

目标条件导航世界模型扩散变换器视觉预见端到端控制隐藏英文摘要

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 为解决目标条件视觉导航问题，本文提出了导航世界动作模型NavWAM。它是一个扩散变换器策略，将未来观测、目标进展值和动作块编码在共享的潜在序列中，从而把导航世界模型的预测直接转化为可执行动作。通过联合学习未来预测与闭环行为所需的动作和价值目标，NavWAM实现了视觉预见与机器人控制的端到端集成。

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图选择动作专家混合隐藏英文摘要

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 在双臂机器人操作中，视觉信息的相关性与双臂交互模式随任务阶段动态变化。现有单体视觉-语言-动作策略难以分别处理这两方面。本文提出基于双级结构分解的VLA框架：视图选择视觉路由器动态调整腕部视图贡献以聚焦关键视觉线索，交互感知动作混合专家将动作生成分解为协调与独立路径，从而适应不同的交互需求。

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

事件感知世界模型长周期操作任务规范机器人想象隐藏英文摘要

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 针对长周期机器人操作中缺乏明确任务进展信号的问题，本文提出了事件感知世界模型EA-WM。该方法在预训练的视觉特征世界模型基础上，引入基于任务规范引导的事件预测与验证机制。它通过结构化事件状态解码与多维度评分（任务进度、语义一致性、物理可行性、不确定性），对候选未来轨迹进行筛选与排序，旨在为机器人操作提供可靠、可解释的想象与决策支持。

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

几何感知置信场内镜粘膜下剥离术机器人辅助手术视觉引导隐藏英文摘要

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 为解决机器人辅助内镜粘膜下剥离术中因烟雾、组织变形等因素导致的精确视觉引导难题，本文提出了几何感知置信场网络GeoCFNet。该方法将剥离引导建模为几何感知的置信场估计问题，旨在同时预测首选剥离区域及其向周围组织的空间过渡，以维持安全的剥离通道和组织边界，从而提升手术的稳定性和精确性。

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

视觉-语言-动作模型协作操作模仿学习人机协作动作分块隐藏英文摘要

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 本文研究了基于视觉-语言-动作模型（VLA）的端到端模仿学习在隐式人机协作中的应用。作者评估了当前先进模型，发现其动作分块策略在协作任务中存在“演示动作泄漏”导致过早协助的失败模式，并分析了该问题在长执行周期任务中的加剧。研究为理解与改进VLA模型在真实协作场景下的表现提供了关键洞察。

Mana: Dexterous Manipulation of Articulated Tools

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

关节工具灵巧操作动画框架仿真到现实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 关节工具的操作因需协调内部自由度和复杂的接触交互而极具挑战。本文提出Mana框架，将灵巧操作重新诠释为一个动画问题。该框架采用粗到细的流水线，通过运动规划和强化学习，将程序化生成的抓取关键帧转换为操作轨迹，仅需少量用户交互即可为不同工具生成功能性抓取策略，并成功实现从仿真到现实的迁移。

$\texttt{WEAVER}$, 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

世界模型机器人操作高保真一致性效率隐藏英文摘要

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 为支持机器人的策略评估、改进和规划等下游任务，世界模型需同时满足高保真度、长时序一致性和高效生成三个目标。本文提出了WEAVER架构，这是一个多视角世界模型，旨在联合预测未来的隐状态和奖励值。该模型在机器人操作任务上达到了当前最优的性能，为构建有效可靠的机器人学习世界模型提供了新的解决方案。

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

结构先验仿生手灵巧操作解剖学肌腱驱动隐藏英文摘要

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 人类手的灵巧性部分源于其骨骼、韧带、肌腱等物理结构的“结构智能”。本文基于此解剖学先验，提出了MCR仿生手。该设计将结构智能分为“结构先验生成”和“肌肉介导调制”两个层面，前者通过肌腱路径等将低维姿态输入转化为默认抓取配置，后者通过外部肌肉等调节接触状态和力分布，实现了高度仿生的灵巧操作。

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

空间标注可靠性校准机器人演示风险感知自动化标注隐藏英文摘要

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 为大规模机器人演示数据自动生成带可靠性评分的结构化空间标注（如边界框、轨迹），本文提出了SPARC框架。与现有自动化流水线不同，SPARC利用演示数据固有的时空结构，通过风险感知校准，为每个自动标注分配一个可靠的正确性评分，从而在过滤噪声标签和保留有用样本之间提供更优的平衡，服务于策略学习和任务规划。

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

手势理解视觉-语言-动作模型人机交互意图接地多模态隐藏英文摘要

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-动作模型多为纯文本驱动，忽略了手势等非语言线索在人机交互中的作用，可能导致意图理解不准确。本文提出GIVE方法，在不修改VLA模型架构的前提下，通过视觉和语义两条互补通路融入手势信息（如手部骨骼叠加和指尖射线），增强模型对人类手势意图的理解，从而提升在语言指令模糊场景下操作的可靠性。

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

移动操作几何感知动作变换器扩散模型空间编码隐藏英文摘要

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 针对全身移动操作中几何感知和动作生成的挑战，现有方法依赖2D特征或稀疏3D表示，且将手臂与基座控制编码为一体。本文提出GeoHAT，一个端到端扩散框架，采用轻量级傅里叶空间编码器将密集3D坐标映射为几何令牌，并实现几何信息的自适应注入与关注，旨在提升移动操作的性能与效率，适用于服务机器人等领域。

Real-Time Execution with Autoregressive Policies

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

自回归策略实时执行视觉语言动作模型约束解码多轨迹解码隐藏英文摘要

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 实时执行对于大规模视觉语言动作模型的部署至关重要，但自回归策略在同步推理中推出速度较慢。本文演示通过调整分词视界和应用约束解码，自回归策略可实现严格延迟界限的实时执行，并支持多轨迹解码以最大化性能，从而在模拟和真实环境中优于流匹配策略，推动机器人控制等应用。

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

导电纤维应变传感器触觉传感器低成本制造机器人传感隐藏英文摘要

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 现有机器人传感器通常成本高昂。本文提出并实验表征一种基于导电线的低成本、易制造柔性纤维传感器，可作为电阻应变传感器和电容传感器使用。该传感器在机器人抓取触发、姿态估计等应用中展示潜力，有助于降低机器人系统的传感成本并提升灵活性。

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

虚拟夹具肌电信号机器人手术自适应控制各向异性隐藏英文摘要

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 针对机器人辅助手术中传统虚拟夹具固定几何、缺乏适应性的问题，本文提出一个自适应各向异性虚拟夹具框架。通过肌电信号推断外科医生意图，实时调整夹具几何，从而在切除和解剖任务中实现无缝的精准引导与动态约束，提升手术灵活性和精确度。