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。
Runtime failure monitors for robot policies are typically evaluated by how accurately they distinguish failed from successful executions. Yet an accurate alarm may arrive too late for recovery, while an unnecessary intervention may disrupt an execution that would otherwise succeed.
Reinforcement Learning (RL) substantially improves planning performance in autonomous-driving Vision-Language-Action (VLA) models. However, existing RL methods rely on scalar trajectory rewards that measure overall quality but fail to identify specific planning errors or provide actionable correction signals.
Vision-language-action (VLA) models and world action models (WAMs) have advanced robotic policy learning by building on pretrained vision-language models (VLMs) and video generative models (VGMs), respectively. Yet VLAs learn without explicit supervision from future world states, while WAMs that condition actions on predicted futures incur extra inference cost and let visual prediction errors propagate into actions.
Vision-language-action (VLA) policies issue sparse targets whose arrival at the robot controller is delayed and irregular, so a reasonable predicted action can still produce abrupt motion near people. Remedies are evaluated on different streams, delays and metrics.
Memory-dependent manipulation requires a policy to track execution state and recover visual evidence that is no longer observable. We propose dual-stream memory for vision-language-action policies, assigning these requirements to complementary representations.
Pre-trained robot policies, such as Vision-Language-Action (VLA) models, provide a strong foundation for generalist robotic manipulation, but adapting them to new tasks with only a few demonstrations remains challenging. Directly fine-tuning a pre-trained policy often achieves strong in-distribution performance, yet it can distort the representations learned during pre-training and weaken the model's generalization ability under out-of-distribution shifts.
Pretrained vision-language-action (VLA) models encode broad manipulation priors, yet adapting them to specific tasks requires balancing limited robot interaction, computational resources, and safety constraints. Noise space learning (NSL) adapts flow-matching-based VLAs by learning a context-conditioned distribution over noise while keeping the pretrained VLA fixed.
Actions specify what a robot should execute, but do not describe what those commands will achieve. Vision-language-action (VLA) policies nevertheless learn primarily by predicting action chunks from images, language, and state.
A user who brings a generalist manipulation policy into their home or office wants it to pick up their objects from their shelves, counters, and tables. Yet even a strong generalist like $π_{0.5}$ achieves 0% pickup success in our captured deployment scenes without adaptation, and collecting real demonstrations in every user's environment is prohibitively expensive.
Scaling generalist robotic policies is limited by the high cost of collecting embodied interaction data. Large-scale egocentric human videos offer a rich alternative, but they lack robot action labels, and existing action annotation methods are often noisy under real-world visual complexity and diverse manipulation dynamics.
FastOPD是一个预印本框架，通过在线策略蒸馏将大型VLA模型压缩为轻量级少步学生模型。在LIBERO上，仅用2步推理就保留了π0.5约84%的性能，并将推理延迟降低78.1%；在RoboTwin 2.0上单步成功率提升15.9个百分点。该工作大幅降低训练和推理成本，有望推动基础策略在消费级硬件上的部署。
MixVLA是arXiv预印本提出的模型无关训练框架，通过信息瓶颈提取不变表征，并用权重减法得到非不变分支，再对非不变嵌入做自适应混合后与不变表征融合。在LIBERO-Plus和RoboTwin等基准上，该方法提升了零样本分布外任务成功率，且在真实机器人光照扰动下优于π0.5。该框架无需额外数据或改动模型架构，但尚未经过同行评审。
这篇预印本论文诊断了视觉-语言-动作（VLA）模型的一种特定失败模式：指令-动作绑定，即模型能对语言和视觉作出反应，却无法将二者结合起来选择当前任务所需动作。作者提出 Equivariant Counterfactual Training（ECT），通过补充同指令、不同场景、不同动作的成对演示并按对训练，在 LIBERO-PRO 上将 π0.5 的平均 Swap 成功率从 36% 提升至 59%，在真实 UR5e 上将未见位置成功率从 3/40 提高到 35/40。该工作解释了现有数据为何允许这种捷径，并为改进机器人指令跟随提供了训练原则。
机器人AI模型每次预测一段未来动作，执行多少步之后再重新观察环境，过去大多由工程师手动设为固定值。清华大学、北京智源人工智能研究院（BAAI）等机构发布的这篇预印本提出一个即插即用模块，在运行中自动决定这一步数，无需重新训练，就提升了Physical Intelligence和英伟达现有模型在仿真和真实双臂机器人上的成功率。
Vision-language-action (VLA) models have advanced robotic manipulation, but their zero-shot generalization in new tasks and environments remains limited, and their reliance on specialized training keeps them from benefiting directly from rapidly advancing general-purpose vision-language models (VLMs). In parallel, recent agentic robotic systems leverage VLMs for high-level reasoning or coding agents for robot control, but often depend on extensive external models and tools, introducing additional complexity and cost.
Visual Language Action (VLA) models offer unprecedented generalization for autonomous robots; however, their real-world deployment is frequently bottlenecked by unreliable execution and the prohibitive computational cost of fine-tuning for specific robot embodiments and tasks. To bridge this gap, we propose WayFinder, an end-to-end, closed-loop hierarchical VLA framework that circumvents the need for fine-tuning by decoupling high-level task reasoning from low-level kinematic control.
Vision-language-action and world-action models have demonstrated impressive capabilities in robotics, yet generalization to unseen tasks remains challenging. More recently, general-purpose multimodal agents have shown great potential for zero-shot robotic task solving.
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.
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.
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。