SABER：一种可扩展的动作基于具身数据集用于真实世界VLA适应
SABER: A Scalable Action-Based Embodied Dataset for Real-World VLA Adaptation
AI总结：
SABER通过高保真零售机器人动作数据集提升真实世界零售任务的机器人性能，采用多流动作表示方法，实现29.3%的成功率，优于微调基线。
摘要：
现实世界中机器人部署依赖丰富且特定领域的动作数据，如同强大的模型架构一样。通用机器人基础模型在复杂未见过的任务如零售领域操作时表现有限，根本原因是数据缺口：零售环境在通用机器人预训练分布中缺失，通过远程操作填补这一缺口是成本高昂、物流受限且难以扩展的。我们引入SABER，一个由超过100小时自然店内捕捉数据构建的高保真零售机器人动作数据集，涵盖多个真实超市环境。第一人称摄像头记录交互点的精细手部活动，而DreamVu的ALIA摄像头的360度场景视频同时观察整个空间的所有行为者和活动。这种组合产生了一个独特完整的零售人类行为图景：灵活的手部活动、全身运动和场景动态，均在无布景、无剧本或远程操作的情况下捕捉。SABER数据集包含44.8K个训练样本，跨越三个动作表示流：25K个通过LAPA风格编码的潜在动作序列，18.6K个经过重新定位到机器人关节空间的灵活手部姿态轨迹，以及1.2K个重新定位到人形 embodiment 的全身同步运动序列。当应用于GR00T N1.6通过共享骨干多任务后训练配方时，SABER在十个零售操作任务上实现了29.3%的平均成功率，比微调基线高出2.19倍。SABER证明了通往有能力零售机器人的路径是通过更好的数据，这些数据今天可以收集，规模扩大，而无需机器人在环中。数据集和代码可在https://dreamvu.ai/saber获取。
英文摘要：
Robotic deployment in real-world environments depends on rich, domain-specific action data as much as on strong model architecture. General-purpose robot foundation models show modest performance in complex unseen tasks such as manipulation in a retail domain when applied out of the box. The root cause is a data gap: retail environments are structurally absent from general robot pretraining distributions, and the path to filling that gap through teleoperation is prohibitively expensive, logistically constrained, and difficult to scale. We introduce SABER, a high-fidelity retail robotics action dataset built from over 100 hours of natural in-store capture across multiple real grocery environments. Egocentric footage from head-mounted cameras records fine-grained hand activity at the point of interaction, while exocentric 360-degree scene footage from DreamVu's ALIA camera simultaneously observes all actors and activities across the entire space. This combination yields a uniquely complete picture of human retail behavior: dexterous hand activity, whole-body motion, and scene dynamics, all captured without staging, scripting, or teleoperation overhead. The SABER corpus contains 44.8K training samples across three action representation streams: 25K latent action sequences via LAPA-style encoding, 18.6K dexterous hand-pose trajectories retargeted to robot joint space, and 1.2K whole-body synchronized motion sequences retargeted to a humanoid embodiment. When applied to GR00T N1.6 via a shared-backbone multi-task post-training recipe, SABER yields a mean success rate of 29.3% across ten retail manipulation tasks -- more than 2.19x over fine-tuning baselines (13.4%). SABER demonstrates that the path to capable retail robots runs through better data, which can be collected today, at scale, without a robot in the loop. The dataset and code are available at https://dreamvu.ai/saber
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
