2026-07-02

Structured 4D Latent Predictive Model for Robot Planning

第一作者: Zhiyi Li · 方向: 具身智能 · 来源: cs.RO

07/02 00:52

具身智能规划四维潜空间预测三维几何建模条件逆动力学机器人场景推理隐藏英文摘要

Video predictive models are emerging as a powerful paradigm in robotics, offering a promising path toward task generalization, long-horizon planning, and flexible decision-making. However, prevailing approaches often operate on 2D video sequences, inherently lacking the 3D geometric understanding necessary for precise spatial reasoning and physical consistency. We introduce a Structured 4D Latent Predictive Model, which predicts the evolution of a scene's 3D structure in a structured latent space conditioned on observations and textual instructions. Our representation encodes the scene holistically and can be decoded into diverse 3D formats, enabling a more complete and 3D consistent scene understanding. This structured 4D latent predictive model serves as a planner, generating future scenes that are translated into executable actions by a goal-conditioned inverse dynamics module...

论文介绍 针对传统视频预测模型缺乏三维几何理解而限制机器人推理能力的问题，本研究提出结构化四维潜空间预测模型。该模型结合观测与文本指令，在潜在空间内推演场景结构演化并生成物理一致的三维序列。作为规划器，其输出经逆动力学模块转化为动作，为具身智能系统的长程任务泛化提供新范式。

Where Am I? Semantic Map Grounding via Vision-Language Models for Multi-Modal Localization

第一作者: Suraj Borate · 方向: 多模态具身 · 来源: cs.RO

07/01 23:37

语义定位视觉语言模型多模态融合机器人导航位姿估计隐藏英文摘要

We address robot localization in GPS-denied indoor environments by reframing it as a semantic reasoning task rather than a geometric estimation problem. Motivated by how humans localize using object-level cues and labeled maps, we ask whether a vision-language model, given a front camera image, a polar LiDAR scan, and a top-down semantic grid map, can infer the robot pose. We fine-tune Qwen2.5-VL-7B with LoRA and attach a lightweight regression head that predicts continuous pose coordinates (x, y, theta) directly from the final hidden state, bypassing text generation. Training uses a composite position-and-direction loss with curriculum learning on a custom Gazebo dataset of 120,112 samples and 527 scenes. On the in-distribution test set of 18,017 samples, the model achieves 98.23 percent position accuracy, 98.00 percent direction accuracy, 96.75 percent full pose accuracy, a mean...

论文介绍 本文针对无GPS室内环境定位难题，将几何估计转化为语义推理任务。研究融合相机图像、极化激光雷达与语义地图，通过微调大视觉语言模型附加轻量回归头，直接输出连续位姿参数。该范式规避了文本生成延迟，为复杂室内的低成本高精度导航提供了新途径。

Path Planning in Physically Viable World Models

第一作者: Su Ann Low · 方向: 导航与运动 · 来源: cs.RO

07/01 17:19

物理世界模型路径规划地形变化模拟鲁棒导航三维高斯泼溅隐藏英文摘要

Robots deployed in unstructured outdoor environments often plan from scene reconstructions collected before deployment because operators cannot remap large or remote sites before every mission. As a result, robots must make long-horizon planning decisions using stale maps that assume the terrain remains unchanged, even though physical changes to the environment may render previously feasible routes unsafe or unreachable at execution time. We present a physically viable world model for evaluating what-if queries for robot navigation under future terrain change. The system augments reconstructed 3D Gaussian splat scenes with physics-based simulation to generate physically modified versions of the same environment without recollecting sensor data or rebuilding the map. We then implement a terrain-aware planner that accounts for physical events, obstacles, and deformations that are...

论文介绍 面向户外非结构化场景中陈旧地图引发路径失效的问题，研究提出支持物理验证的世界模型。该架构结合三维高斯泼溅与物理仿真引擎，无需重采数据即可重构地形演化结果。基于此构建的地形感知规划器可实时评估动态扰动下的通行可行性，显著提升长期部署的导航安全性。

Domain Arithmetic: One-Shot VLA Adaptation under Environmental Shifts

第一作者: Taewook Kang · 方向: VLA 通用模型 · 来源: cs.RO

07/01 17:13

视觉语言动作模型域自适应权重向量运算单样本学习跨平台迁移隐藏英文摘要

Vision-Language-Action (VLA) models often fail to perform the same learned tasks under environmental shifts, such as changes in camera pose and shifts to a different but similar robot (e.g., from Panda to UR5e). Adapting these models to the shifted environment (i.e., target domain) often requires training on multiple demonstrations for each task, which are costly to collect. To reduce the burden of data curation and training, we propose an analogy-based method that adapts VLA models under environmental shifts through weight vector arithmetic with domain-specific information addition, named Domain ARiThmetic (DART). Unlike prior approaches, DART requires collecting only a single demonstration, enabling efficient adaptation. To accurately isolate domain-specific information for addition, DART performs subspace alignment between singular components in weight vectors to filter out noisy...

论文介绍 针对视觉语言动作模型在环境偏移或异型平台上的泛化瓶颈，研究提出一种单次演示驱动的域自适应算法。该方法经子空间对齐提取领域特征，执行权重向量算术运算实现模型快速迁移。此范式大幅削减数据标注开销，为跨场景具身智能部署提供轻量化适配方案。

From Real-Time Planning to Reliable Execution:Scalable Coordination for Heterogeneous Multi-Robot Fleets in Industrial Environments

第一作者: Bo Cao · 方向: 导航与运动 · 来源: cs.RO

07/01 16:16

异构机器人集群在线协调控制冲突消减机制鲁棒执行工业导航隐藏英文摘要

With the increasing deployment of heterogeneous robot fleets in industrial environments, efficient coordination remains a critical challenge. Real-time path planning must simultaneously accommodate high robot densities and heterogeneous motion capabilities, while communication delays, execution uncertainties, and other disturbances may cause robots to deviate from the temporal assumptions underlying planned paths. Such deviations can lead to excessive waiting and congestion propagation across the fleet. This paper presents SCALE, a reactive online coordination framework that enables real-time planning while maintaining robust execution. Within this framework, we introduce a motion-induced conflict reduction mechanism to support the online generation of feasible paths for online conflict resolution. To mitigate the effects of disturbances, we further design a generalized Conjugate...

论文介绍 面向工业场景中异构机器人集群的高密度协同挑战，研究提出一种响应式在线协调框架。系统集成运动诱导冲突消减机制与广义共轭梯度优化策略，在维持实时规划的同时有效抑制通信延迟与执行扰动引发的路径偏离，保障大规模编队的可靠调度与防拥堵运行。

[Preprint] Dynamic Modeling, Gait Synthesis, and Control of a Novel Subsurface Bore Propagator

第一作者: Lina van Brügge · 方向: 具身智能 · 来源: cs.RO

07/01 15:55

地下探测机器人步态合成欧拉-拉格朗日建模模块化控制无人勘探隐藏英文摘要

In this article, we present dynamic modeling, gait synthesis, and feedback control design for a modular novel subsurface robot, designed for human-free subsurface exploration and excavation. The subsurface propagator design is based on two major aspects: 1) anchor and propel movement like an earthworm and 2) excavation similar to tunnel boring machines. This design is decoupled into five separate modules: one drill head to excavate and create cavity for propagation, two modules to anchor the robot, and two modules to enable propagation of the body. In order to design a controller for each of the modules, dynamic models using the Euler-Lagrange framework are developed. These mathematical models are used as a baseline to design controlled decoupled operation of the different joint movements. The operation of robotic assembly is constructed via a centralized state machine for gait...

论文介绍 本文介绍面向无人地下勘探的新型模块化掘进机器人，涵盖其动力学建模与步态控制设计。系统借鉴仿生灵虫锚定推进与盾构开挖机理，将设备解耦为独立作业单元。依托欧拉-拉格朗日方程建立关节动力学基线，配合集中式状态机编排协调步序，实现受限空间内的高效自主掘进。

3D Point World Models: Point Completion Enables More Accurate Dynamics Learning

第一作者: Skand Peri · 方向: 数据集与评测 · 来源: cs.RO

07/01 04:27

三维点云世界模型点云补全动力学预测长视界规划几何一致性隐藏英文摘要

Learning predictive models of the world enables robotic control through planning, potentially allowing robots to improvise solutions on new tasks. However, large video-based dynamics models lack explicit 3D spatial structure and suffer from geometrically inconsistent long-term rollouts with compounding errors. Emerging 3D dynamics models based on partial point clouds improve geometric consistency but remain sensitive to occlusions and accumulated prediction drift. To address these challenges, we present 3D Point World Models (3DPWM) - a task-agnostic world model that operates entirely in 3D space by first completing partial point clouds and then learning action-conditioned dynamics in this completed 3D scene. By operating on completed geometry, 3DPWM enables reliable long-horizon rollouts and more accurate cost evaluation for model-based planning while supporting adaptation to new...

论文介绍 针对视频动力学模型缺乏显式三维结构及局部点云易受遮挡干扰的局限，研究提出完全基于三维空间的点云世界模型。该架构先对稀疏观测进行几何补全，再于此完整拓扑中学习条件动力学演化。此举有效切断累积漂移，显著提升长视界仿真推演的一致性与模型预测控制精度。

FurnitureVLA: Learning Long-Horizon Bimanual Furniture Assembly with Vision-Language-Action Model

第一作者: Chenyang Ma · 方向: VLA 通用模型 · 来源: cs.RO

具身装配任务双臂协作视觉语言动作模型进度增强策略长程规划隐藏英文摘要

Abstract:Current work on robot furniture assembly mostly focuses on toy-scale settings or single-arm manipulation. We introduce FurnitureVLA, the first systematic study of real-scale bimanual furniture assembly using Vision-Language-Action models (VLAs). We formalize the task, develop a scalable simulation pipeline for expert data generation and evaluation, and build a VR teleoperation system for single-operator bimanual control to collect high-quality real-world demonstrations. To address extreme long-horizon assembly with up to 7 subtasks and 1550 control steps, we propose a progress-enhanced VLA, finetuned on semantically grounded subtasks, that jointly predicts actions and a continuous progress signal, enabling automatic subtask transitions and reducing compounding errors during inference. We further study perception and control design factors that critically affect precision in...

论文介绍 本文聚焦真实尺度下双臂家具装配的长程复杂操作，首次系统性探索视觉语言动作模型的应用边界。研究构建可扩展仿真管线与虚拟现实遥操作采集系统，提出进度增强网络架构。该模型联合预测控制指令与连续进度信号，实现子任务无缝切换与误差抑制，为高复杂度具身制造提供新范式。

FAR: Failure-Aware Retry for Test-Time Recovery and Continual Policy Improvement

第一作者: Haoran Hao · 方向: 机器人操作 · 来源: cs.RO

测试时恢复故障感知重试偏好对齐持续策略优化具身容错隐藏英文摘要

Abstract:Robot policies inevitably encounter failures when deployed in real environments. Naive retries often repeat the same mistakes, while many existing recovery methods rely on human intervention. In this paper, we propose Failure-Aware Retry (FAR), a framework that enables robots to learn from previous failures at test time, adapt their behavior accordingly, and eventually complete the task autonomously. FAR combines Failure-Contrastive Preference Adaptation, which constructs preference learning data from failures to steer the policy away from previously unsuccessful behaviors, with lightweight action perturbations during retries to encourage local exploration. We further incorporate successful recovery trajectories into a training loop for continual policy improvement. Experiments in both simulation and real-world manipulation tasks show that FAR substantially improves success...

论文介绍 针对机器人实际部署中操作失败且传统重试策略无效的问题，研究提出故障感知重试框架。该机制在测试期利用对比偏好适应技术引导策略规避历史错误，并结合轻量子探索激发新轨迹。成功恢复经验被反哺训练循环以实现持续进化，显著增强系统在物理交互中的自纠错能力。

Technical Report: Asynchronous Distributed Trajectory Estimation of Multi-Robot Systems

第一作者: Adam Pooley · 方向: 导航与运动 · 来源: cs.RO

分布式轨迹估计异步算法多机器人系统坐标下降法隐藏英文摘要

Abstract:Distributed trajectory estimation arises in many applications across robotics, but existing implementations typically do not consider asynchrony in agents' communications and computations. Therefore, we propose an asynchronous block coordinate descent algorithm for distributed trajectory estimation. We consider a team of agents that observes a team of robots and estimates their states over a sliding window. The agents solve an approximation of the maximum a posteriori estimation problem, which we derive. We show this approximation introduces negligible errors and eliminates up to 96.9% of communications among agents. Next, we prove that agents' iterates converge exponentially fast to the optimal estimate of the robots' states. Simulations show that this approach has up to 64% less error than a comparable state-of-the-art algorithm. Experiments on mobile robots show the...

论文介绍 针对多机器人网络中通信与计算异步性制约轨迹估计效率的问题，本文提出异步块坐标下降算法。该方法在滑窗内求解最大后验近似问题，在几乎不增加误差的前提下显著降低通信开销，并严格证明迭代过程的指数收敛性。该工作可为大规模多机协同导航与状态估计提供高效计算框架。

ROSA: A Robotics Foundation Model Serving System for Robot Factories

第一作者: Wenqi Jiang · 方向: 具身智能 · 来源: cs.RO

机器人基础模型推理服务系统共享GPU池机器人工厂隐藏英文摘要

Abstract:Robotics foundation models (RFMs) are making general-purpose robots increasingly practical for factory deployments. While RFM serving systems are central to this vision, existing systems are largely shaped by a single-robot, single-model assumption: inference is treated as an edge-computing problem handled by an on-robot or dedicated nearby GPU, and the serving objective is to minimize the latency of a single action model. In this paper, we propose ROSA, an RFM serving system for robot factories designed around three key principles. First, ROSA adopts shared GPU-pool serving, allowing a fleet of robots to access powerful server-class GPUs over the network in order to improve inference performance, battery duration, and GPU utilization. Second, ROSA provides a robotics-aware programming abstraction and system design that supports multi-model pipelines, per-task performance...

论文介绍 面向机器人工厂中具身智能模型的部署需求，现有边缘计算方案难以满足多机协同与多模型管线的高效推理要求。本文提出ROSA服务系统，采用跨网络共享GPU池架构以优化推理性能与能耗，并提供面向机器人的编程抽象与系统设计。该系统可支持多模型流水线调度，助力通用机器人规模化工业落地。

Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation

第一作者: Chi Zhang · 方向: VLA 通用模型 · 来源: cs.RO

触觉预训练灵巧操作VLA模型人机对齐隐藏英文摘要

Abstract:As an essential modality for dexterous and contact-rich tasks, tactile sensing provides precise force feedback that cannot be reliably inferred from vision. However, limited by hardware and data collection systems, existing datasets with tactility remain small in scale and narrow in contact coverage. Meanwhile, Vision-Language-Action (VLA) models with tactile modality are constrained on dynamics-agnostic post-training, which limits the performance ceiling on downstream tasks. In this paper, we present H-Tac, a large-scale tactile-action dataset with 160-hour egocentric human videos containing more than 300 tasks and 135k episodes. Building upon this, we propose Transferable Tactile Pre-Training (TTP), a system of tactile-based pre-training on human data for fine-grained robotic tasks. To bridge the gap between humans and robots, we use unified tactile and action spaces...

论文介绍 针对灵巧操作中触觉数据稀缺且模型动态对齐不足的问题，本文发布H-Tac数据集并提出可迁移触觉预训练方法。该方法构建统一触觉与动作空间弥合人机差异，实现基于人类数据的触觉得到预训练。研究成果可显著提升复杂接触任务的策略泛化能力与微调性能。

RoboWorld: Fast and Reliable Neural Simulators for Generalist Robot Policy Evaluation

第一作者: Byeongguk Jeon · 方向: VLA 通用模型 · 来源: cs.RO

世界模型机器人策略评估自回归生成自动评测流水线隐藏英文摘要

Abstract:Video world models are emerging as a scalable alternative for evaluating generalist robot policies, bypassing the physical constraints and engineering burdens of real-world deployment. However, evaluating policies with video world models remains challenging, as world-model errors can make generated rollouts unreliable and slow inference limits large-scale throughput. We introduce RoboWorld, an automated evaluation pipeline that pairs a fast autoregressive video world model with a task-progress-aware vision-language model scoring. To enable reliable long-horizon autoregressive world-model rollouts, we propose Step Forcing, which combines anchored and one-step self-forwarded contexts to reduce train--test mismatch while preserving action--observation dynamics. Together, these components enable RoboWorld to align strongly with real-world robot evaluation across tasks and...

论文介绍 为突破真实物理环境对策略评估的效率限制，本文提出RoboWorld自动化评测流水线。该流水线结合自回归视频世界模型与任务进度感知评分器，并引入Step Forcing机制抑制长程生成的分布偏移。该方法可实现高吞吐虚拟仿真验证，有效对齐实地测试效果。

AutoSpeed: Annotation-Free Stage-Adaptive Motion Speed Learning for Robot Manipulation

第一作者: Qingda Hu · 方向: 机器人操作 · 来源: cs.RO

速度自适应学习机器人操作时序预测视界无标注优化隐藏英文摘要

Abstract:Different stages of manipulation tasks exhibit varying levels of difficulty, suggesting stage-dependent motion speeds and temporal prediction horizons. However, existing IL-based visuomotor policies typically imitate the execution speed of expert demonstrations and operate with a fixed temporal prediction horizon, limiting flexibility and overall task throughput. In this paper, we introduce AutoSpeed, a model-agnostic learning framework that enables existing visuomotor policies to predict trajectories with stage-adaptive motion speeds, without requiring speed or stage annotations. We treat future trajectories at different speeds as candidate optimization targets, evaluate each candidate using a composite cost that trades off prediction error against prediction horizon, and optimize the policy toward the minimum-cost candidate. With a fixed-length action sequence, speed...

论文介绍 针对传统视觉运动策略固定速度与预测视界导致灵活性不足的问题，本文提出AutoSpeed无标注阶段自适应速度学习框架。该框架将不同速度未来轨迹作为候选目标，通过综合评估预测误差与时界代价进行联合优化。该方法可使策略自动生成随阶段变化的运动速度，有效提升机械臂操作吞吐量。

Robots Ask the Way: Communication-Enabled Social Navigation

第一作者: Valentino Sacco · 方向: 导航与运动 · 来源: cs.RO

社交导航人机通信主动信息获取多智能体环境隐藏英文摘要

Abstract:Assistive autonomous robots operating in multi-agent environments require efficient strategies to locate specific individuals among multiple residents. Current social navigation methods focus on reactive collision avoidance and trajectory adaptation, but lack mechanisms to proactively gather information through human-robot communication. We introduce Communication-enabled Social Navigation (CommNav). In this novel task, robotic agents actively seek assistance from residents to locate target individuals by requesting information about recent sightings, locations, and movements. To evaluate CommNav, we extend Habitat 3.0 to create Habitat 3.0c, a communication-enabled variant supporting multi-human environments with information exchange protocols. Adding our communication module (COMM) to a state-of-the-art social navigation model yields a 10 percentage-point improvement in...

论文介绍 为克服现有社交导航缺乏主动信息交互的局限，本文提出通信增强社交导航框架。机器人在多智能体环境中主动向人员询问目标位置与动态以实现精准寻人。配套开发的通信扩展仿真平台支持多人类交互协议验证。研究表明集成通信模块可显著提升复杂场景导航成功率。