2026-06-15

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

视觉导航世界模型动作模型扩散变换器机器人控制隐藏英文摘要

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 目标条件视觉导航需将预测转换为控制，NavWAM提出扩散变换器策略，联合学习未来观察、目标进度值和动作块。通过模拟预训练和实际适应，使视觉预测直接驱动机器人闭环控制，无需外部规划器，简化导航系统并提高效率。

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混合专家自适应隐藏英文摘要

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 双臂机器人操作中视觉信息和交互模式多变，现有VLA模型处理不足。本文提出双级结构分解框架，包括视觉路由器和动作混合专家，动态调整视觉输入贡献，分解动作为协调和独立路径，以适应双臂操作的不同阶段，提高策略的自适应能力。

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长 horizon 操作事件预测验证隐藏英文摘要

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 长 horizon 操作需任务进展信号，EA-WM增强预训练世界模型 with 任务规范接地的事件预测和验证。它滚动候选未来，解码为结构化事件状态，基于任务进度、语义一致性、物理可行性和不确定性评分，为机器人想象提供可靠性保障，确保操作安全。

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

机器人辅助手术置信场几何感知内镜黏膜下剥离内镜场景挑战隐藏英文摘要

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 本文研究了机器人辅助内镜黏膜下剥离中的视觉引导问题。在烟雾、反光、组织变形等复杂内镜场景下，准确估计描述解剖区域和安全边界的置信场极具挑战。作者提出了几何感知置信场网络 GeoCFNet，该方法通过结合几何信息来提升动态手术场景中的置信场估计可靠性，旨在为机器人手术提供稳定精确的视觉引导，以维持安全的解剖分离路径。

EgoEngine: From Egocentric Human Videos to High-Fidelity Dexterous Robot Demonstrations

第一作者: Yangcen Liu · 方向: 机器人操作 · 来源: cs.RO

06/11 03:01

自我中心视角灵巧操作数据生成域差距仿真数据隐藏英文摘要

Dexterous manipulation is limited by the cost of collecting large-scale robot demonstrations. Egocentric human videos offer a scalable source of diverse manipulation behaviors, but directly using them for robot learning requires bridging two gaps: the visual gap between human and robot observations, and the action gap between human motion and robot-executable action. We propose EgoEngine, a scalable framework for transforming egocentric human manipulation videos into high-fidelity robot data. Given an egocentric RGB video, EgoEngine produces: (i) a high-fidelity robot observation video replacing human with robot while preserving scene context and temporal alignment, and (ii) a task-aligned, executable robot action trajectory under feasibility constraints. Experiments in simulation and on real robots show that EgoEngine enables scalable conversion of human videos into robot data and, to...

论文介绍 灵巧操作机器人学习受限于大规模机器人演示数据的收集成本。本文提出了 EgoEngine 框架，旨在将低成本、可扩展的自我中心人类操作视频转换为高保真的机器人演示数据。该方法通过生成包含机器人视角的观测视频和对齐任务的可执行动作轨迹，来弥合人类与机器人之间的视觉和动作域差距，为机器人策略学习提供了新的数据来源。

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

人机协作视觉语言动作模型模仿学习动作块端到端学习隐藏英文摘要

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 现有人机协作系统常依赖手工设计的流程，限制了其可扩展性。本文探索了通过模仿学习训练的端到端视觉语言动作模型来支持隐式人机协作操作。研究评估了当前先进模型，发现动作块策略在隐式协作中存在「演示动作泄漏」问题，即动作块跨越了潜在的任务转换点，导致机器人过早执行辅助行为。该工作分析了影响此类系统实际性能的关键因素。

Mana: Dexterous Manipulation of Articulated Tools

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

铰接工具灵巧操作仿真到真实关键帧动画运动规划隐藏英文摘要

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 铰接工具因其内部自由度与丰富的接触交互，对灵巧机器人操作构成挑战。本文提出了 Mana 框架，将灵巧操作重新定义为动画问题。该框架采用从粗到精的流程：首先自动生成抓取关键帧，随后通过运动规划和强化学习将其转化为操作轨迹。这种方法实现了数据的自动化生成，只需少量交互即可为不同工具指定功能，为解决铰接工具操作提供了通用仿真到真实的路径。

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

泛化策略流匹配流程逆转引导动作引导蒸馏隐藏英文摘要

Abstract:Generalist policies can learn a wide range of skills from diverse robot datasets. In order to solve or improve on challenging new 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...

论文介绍 通用机器人策略能从多样数据中学习广泛技能，但在面对新任务时可能无法直接输出理想动作。本文针对流匹配类泛化策略，提出了流程逆转引导方法。该方法能从人类或视觉语言模型提供的粗糙语义引导或次优但合理的动作中，通过反向流程找到其潜在噪声，并映射回策略的良好动作模式，从而改进零样本控制性能，并可通过行为克隆将改进效果蒸馏到新策略中。

$\texttt{WEAVER}$, 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保真度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 高质量的世界模型对机器人操作中的策略评估、改进和规划至关重要。本文提出了 WEAVER，一种多视角世界模型架构，旨在同时满足高保真度、长时一致性和高效率三个关键要求。该模型通过预测未来的潜在状态和奖励值，在机器人操作任务上达到了先进性能，为以有限真实世界交互来解锁世界模型的下游应用潜力提供了有效工具。

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

仿生手结构先验灵巧操作生物力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 人类手部灵巧性部分源于其骨骼、韧带和肌腱等物理结构编码的「结构智能」。本文基于此认识，提出了 MCR 仿生手设计。该设计利用了两种结构先验：一是由肌腱路由等结构生成默认抓取构型，二是由肌肉系统对姿态和接触状态进行调制。通过1:1 模拟肌肉骨骼系统，该仿生手旨在实现更自然、更高效的灵巧操作。

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

机器人演示空间标注可靠性校准自动化标注风险感知隐藏英文摘要

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 为机器人演示自动生成结构化空间标注对多种应用有益。现有自动化流程缺乏可靠的质量信号。本文提出了 SPARC 框架，它利用演示数据固有的时空结构，自动为机器人演示生成如边界框、物体轨迹和操作阶段标签等结构化空间标注，并为每个标注分配一个经校准的可靠性评分。这种风险感知的标注方式为下游机器人学习和任务组合提供了更可靠的数据基础。

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

视觉-语言-动作模型手势理解人机交互意图接地隐藏英文摘要

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 本文针对当前视觉-语言-动作（VLA）模型在机器人操作中忽略手势等非语言线索的问题，提出GIVE方法。该方法通过视觉路径（叠加手部骨架和指尖射线）和语义路径，在不修改模型架构的情况下增强对手势的理解，从而在语言指令模糊时提升意图接地和操作可靠性，适用于人机交互场景。

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

几何自适应混合动作Transformer移动操作扩散框架隐藏英文摘要

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 本文解决全身移动操作中几何感知与动作生成的协调难题。提出GeoHAT端到端扩散框架，采用轻量傅里叶空间编码器将密集3D坐标注入为几何token，并运用混合注意力机制，在可靠时注入几何信息，在需要时关注，以提升机器人在变化视点下的操作性能。

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-动作模型延迟界限隐藏英文摘要

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 本文探讨大规模视觉-语言-动作模型的实时执行问题，提出针对自回归策略的方法。通过调整tokenization horizon和应用约束解码，保证严格延迟界限，实现多轨迹解码以最大化性能，在模拟和真实环境中表现优于流匹配策略，促进机器人部署。

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

低成本传感器应变传感触觉传感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 本文针对机器人传感器成本高、制造复杂的问题，提出一种低成本、易制造的柔性导电纤维。该纤维使用廉价商业部件制成，可作为电阻应变传感器和电容触觉传感器，应用于气动辅助手指抓取、机器人带姿态估计和柔性固体姿态估计等场景。