机器人与具身智能领域的最新论文，以及每篇对产业的意义。
这篇arXiv预印本提出Encore框架，将遥操作演示蒸馏为编码智能体可查询的证据包，让智能体编写、运行并迭代修复策略程序，运行时不调用模型。在LIBERO-PRO的60个扰动单元上，3条演示的冻结程序在3000个密封回合中成功2889次（96.3%），无演示为2846次（94.9%）；在RoboDojo上无演示全部失败，3条演示在前10个任务取得95/450成功。双臂真机上完成手递手和杯子倒扣，各9/10成功。
这篇预印本提出自补偿VLA，在部署时利用指令与实际运动残差在线微调LoRA，让策略预补偿机器人执行误差。在RoboStress仿真基准和两台Piper机械臂上，该方法平均任务成功率相比基线提升超过30个百分点（真机），并在未见物体上保持提升。这为提升VLA在真实机器人上的长期可靠性提供了不依赖任务奖励的部署期适应方案。
Embodied systems must make knowledge acquired during one encounter usable in another despite changes in viewpoint, motion, and illumination. Yet aggregate cross-video accuracy conflates failures of local perception with failures to preserve observation identity, establish correspondence, and compose evidence, obscuring whether local video understanding actually transfers.
We propose a QCQP-representable IMU pre-integration factor that enables certifiable estimation with pre-integrated inertial measurements. To the best of our knowledge, this is the first work to directly incorporate IMU pre-integration into certifiable estimation.
While contact-rich manipulation requires deliberate regulation of interaction forces, recent approaches to robot manipulation learning predominantly represent actions as target positions or poses. Even methods that incorporate force sensing either use it solely as an observation or, when predicting forces as part of the output, rely on a hybrid force controller.
Large language models (LLMs) are increasingly used as high-level planners in robot navigation, but their outputs may become unreliable when instructions are ambiguous, unsupported by the environment, or semantically inconsistent. This paper presents a Risk-Aware Semantic Grounding framework for trustworthy LLM-based robot planning.
In this preprint, the authors build an onboard perception pipeline that lets a lightweight Explorer robot find minerals in underground mines from free-text prompts and hand compact, oriented 3D inspection targets to a heavier Inspector robot. Field tests in a test mine and an active magnesite mine detected two mineral types without retraining and condensed about 38,000 raw mineral points into a few dozen polygons, roughly two orders of magnitude smaller. This matters because autonomous mining inspection requires no mine-specific labels and low-bandwidth robot-to-robot handover.
Vision-language models can coordinate long-horizon robot manipulation, yet successful task reasoning still depends on whether local physical interactions produce the intended effects. We study how repeated interaction can improve this capability without updating the base model.
Grinding and sanding are fundamental processes in industrial robotic surface finishing. However, physical trials are expensive and consume workpieces, making reproducible experiments difficult.
Robots deployed in the physical world must be able to improve beyond their initial training as they encounter new situations and failures. For this improvement to scale across tasks, it must make effective use of experience without requiring human demonstration of each correction.
Diffusion and flow-matching Vision-Language-Action (VLA) policies generate action chunks through iterative denoising, incurring substantial inference latency that severely limits real-time robotic control. Existing acceleration methods treat an action chunk as a monolithic computational unit, ignoring a crucial physical reality of receding-horizon control: actions are generated jointly but consumed sequentially, resulting in inherently heterogeneous execution urgencies.
Video-based policy learning is particularly promising, as it illustrates target behaviors without requiring action annotations or embodiment-matched demonstrations. A central challenge is deciding what information should be transferred from the video to the robot.
When interacting with an unfamiliar deformable material, a robot lacks prior knowledge of its physical properties and how it will respond to applied forces and motion. Rapid online identification is therefore essential for reliable manipulation.
Robotic foundation models (RFMs) are increasingly capable of general-purpose manipulation, yet reliable physical interaction remains challenging in contact-rich settings. We present FP2, a lightweight downstream interface that equips task-adapted RFMs with explicit force control while preserving their action-generation capability.
Simulated manipulation benchmarks are the standard tool for evaluating vision-language-action (VLA) policies and the acceleration methods that reduce their inference latency for on-robot deployment. On these benchmarks, we observe that some training-free acceleration methods, which approximate the baseline policy's computation, achieve higher measured success rates than the baseline itself.
World-action models (WAMs) jointly predict how a scene will evolve and how an agent should act, however joint generation alone does not necessarily impose a shared geometric constraint on these predictions. We present PhysWAM, a unified world-action model for autonomous driving that co-denoises multiview video, metric depth, and ego motion within a single flow-matching transformer.
Test-time adaptation for vision-language navigation (TTA-VLN) enables pretrained policies to adapt online to unseen environments using only test-time observations and interaction history. However, distribution shifts can distort local action preferences and lead to off-course decisions.
Improving robot policies on new tasks without collecting additional expert demonstrations remains a central challenge in robot learning. World action models (WAMs) use broad video priors to jointly predict future videos and actions, offering a potential source of supervision for adapting to new tasks.
DROM是一种语言引导的扩散框架，利用DMP从少量演示生成合成数据，训练单个扩散模型执行17种操作技能。在Franka Emika Panda、FANUC CRX25ia和MuJoCo仿真中，DROM实现多技能泛化和长程任务组合，最佳任务整体成功率86.0%。该工作为预印本，尚未经过同行评审。
Vision-language-action (VLA) models typically operate on RGB images produced by a fixed camera image signal processor (ISP), leaving the imaging pipeline outside the learning and evaluation loop. We systematically examine the consequences of this overlooked design choice across five fundamental ISP dimensions: gain, sensor noise, chromatic response, tonal response, and bit depth.
论文来自 arXiv 机器人领域。尚未生成摘要的论文，显示摘要开头部分。