DexPort：手持式灵巧手数据采集接口，野外数据将成功率提至78%
DexPort: A Hand-Held Skill Interface for Dexterous Hand Data Collection
一组研究者做了个手持设备 DexPort，不用机器人也能采灵巧手演示数据，野外数据把成功率从 36% 拉到 78%，做机器人操控的可以看看。
arXiv 论文 DexPort 提出一种便携式数据采集接口，专为高自由度机器人灵巧手设计，采集与部署使用同一套手部硬件，避免具身差异。操作者只需移动手持装置控制机器人腕部动作，再选择或调整内置技能来协调手指运动，无需逐个控制关节。团队用该设备在实验室和非结构化野外环境共采集超过 2,500 条演示，覆盖拾取放置、手中拧螺丝、抽屉收纳和双臂扫描 4 类任务。行为克隆训练的视觉运动策略显示：使用记录的机器人指令而非状态，成功率从 11% 升至 64%；加入野外数据后任务成功率从 36% 升至 78%。
DexPort: A Hand-Held Skill Interface for Dexterous Hand Data Collection
Collecting high-quality demonstrations remains a central bottleneck for learning dexterous manipulation. Teleoperation records executable robot actions, but it requires a complete robot setup and places the burden of coordinating many finger joints on the operator. Human demonstrations are easier to collect, but their motions must be estimated and retargeted to the robot, and they do not directly provide robot actions. In this paper, we introduce DexPort, a portable data-collection interface for high-DoF robot hands. It uses the same hand hardware for collection and deployment, avoiding a hand embodiment gap, and records both commanded joint targets and measured joint states. Our key observation is that a small set of skills can support a wide range of dexterous tasks. During collection, the operator moves the hand-held interface to specify the robot wrist motion and selects or adjusts skills that coordinate the finger motion. This removes the need to control each joint independently while retaining behaviors such as precise grasping and in-hand manipulation. We collect demonstrations across several real-world tasks and in unstructured environments without a robot arm. We then use behavior cloning to train visuomotor policies directly on the recorded wrist motions and hand commands, without skill labels. We evaluate across 4 tasks: pick-and-place with diverse objects, screwing with in-hand tool use, drawer stowage which requires simultaneous grasp and pulling, and a bimanual scanning task. We collect over 2,500 demonstrations with our portable device, in both laboratory and in-the-wild settings. Across our tasks, we see an improvement from 11% to 64% when using recorded robot commands instead of states, and an improvement from 36% to 78% for task success when using in-the-wild data.