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。
该预印本提出一种电池感知强化学习控制方法，在38克Crazyflie Brushless真机上联合增加推力指令范围和滤波电压输入，同时保留飞控的电压补偿和速率控制。硬件实验中，3.84 m/s圆周跟踪误差较Stock降低49.3%，20圈赛道时间从106.22秒降至95.24秒。相比同样增加指令范围但不使用电压输入的High，误差降低15.3%、赛道时间快4.5%。结果表明显式电压信息能补充飞控原有补偿。
Autonomous vehicles interacting with passengers through natural language must reason beyond immediate commands. Passenger intent may span multiple stages of behavior, depend on future events, refer to surrounding agents or landmarks, and remain relevant as driving conditions evolve.
Search and Rescue (SAR) operations increasingly deploy heterogeneous teams of aerial and ground robots. However, conventional coverage methods typically do not translate perceived terrain into platform-specific reachability, while continuous image exchange imposes a high communication cost.
Latent world models learn action-conditioned dynamics in representation space and often score candidate actions by Euclidean distance to a goal representation. Joint training typically regularizes the representation to prevent collapse, but the resulting representation geometry also determines how terminal errors are weighted during planning.
Teaching humanoids loco-manipulation skills, such as carrying diverse objects, via visual imitation is a promising path toward generalist robots. However, collecting diverse, high-quality interaction videos, such as clips that clearly show a person's full body and unoccluded interactions with objects, poses a practical barrier to scaling this approach.
Robotic foundation models offer a promising path toward general-purpose humanoid robot control, often through hierarchical architectures. However, their effectiveness depends on the command interface between the planner and the controller, which must support accurate execution while remaining easy to predict, and ideally allow new behaviors to be composed from prior ones.
Generative modeling is widely used for producing diverse objects from complex, multimodal distributions. However, its expressivity does not, in general, come with formal guarantees that the generated objects satisfy hard constraints or requirements.
Constrained Reinforcement Learning has recently gained increasing attention in the field of Safe Autonomous Driving, where the general mechanism is to maximize the expected reward while keeping the overall action risk bounded. In this way, the safety issues arising in AD can be mitigated through constrained actions.
Real-world robot learning is constrained by the cost of collecting experience and evaluating candidate behaviors. Video generation models offer a scalable foundation for visual simulators that predict action outcomes before physical execution.
We study robotic in-context learning (ICL), an emerging paradigm that enables robots to infer and execute tasks from visual demonstrations. Despite its growing promise, the problem itself remains under-defined: a visual demonstration simultaneously conveys action trajectories, object semantics, manipulation affordances, spatial relations, and task goals, making it unclear what information the robot is actually expected to follow.
预印本提出DEWO，一种针对World-Action Models的部署后学习范式，通过识别交互转折点，从匹配的成功和失败视觉未来中持续更新世界模型，并在推理时用进度门控引导动作。在五个DexJoCo仿真任务中，DEWO将FastWAM平均成功率从80.7%提升到82.3%，将FACT从64.5%提升到81.2%；在四个真实世界双手灵巧操作任务中，两轮部署学习使初始有成功单元的成功率从51.0%提升到71.7%。论文尚未经同行评审。
Robust and reliable perception is essential for autonomous robots operating in real-world environments, particularly in long-term missions where environmental conditions may change significantly over time. Although recent advances in Visual Foundation Models (VFMs) have improved open-vocabulary semantic segmentation, these models can still suffer from domain shift, which can significantly degrade performance if they are not adapted to the current environment.
能用一个提示让人形机器人模仿舞蹈、摆出姿态或追逐奖励的全身控制模型，每台机器人通常要花100多个GPU小时训练。VinRobotics、新加坡国立大学和德国达姆施塔特工业大学的这篇预印本，把为宇树G1训练的模型迁移到加速进化（Booster）T1、傅利叶N1和团队自研的M3上，只需不到1小时的共享编码器训练加每台不到10个GPU小时，跟踪精度与专用控制器相差无几。
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.
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.
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。