UMI‑Live is an open‑source, full‑stack bimanual manipulation system that lets human experts demonstrate tasks for robot learning without needing teleoperation of physical robots. The custom hardware integrates camera, LiDAR‑inertial sensing, onboard compute, and power, eliminating external tracking or calibration, while a user interface records data and an asynchronous pipeline performs SLAM reconstruction to produce training‑ready datasets. The system supports dual‑YAM teleoperation and VLA policy training, and the authors released 40 hours of data across three tasks, demonstrating high success rates on a pick‑and‑place benchmark.
Universal Manipulation Interface (UMI) devices allow human experts to demonstrate tasks for robot learning without teleoperating physical robots. Using these demonstrations as training data still requ...
arXiv:2610.08220v1 Announce Type: cross Abstract: Portable mobile-manipulation demonstrations can help alleviate data scarcity for embodied intelligence, but obtaining reliable, low-cost, and robot-f...
HIL-UMI is a policy-guided Universal Manipulation Interface that enables robot‑free, human‑in‑the‑loop post‑training of vision‑language‑action models. By querying the current policy during handheld demonstrations and using an Energy Score to detect out‑of‑distribution states, it selectively collects new data and refines a progress‑based advantage estimator. The updated estimator then drives advantage‑conditioned behavioral cloning, improving performance on long‑horizon and precise manipulation tasks while reducing per‑frame collection time compared to HG‑DAgger.
arXiv:2606. 10244v1 Announce Type: cross Abstract: We introduce Yielding Universal Bidigital Interface (YUBI), a finger-aligned gripper designed to enable intuitive, ergonomic, and scalable data collection for bimanual dexterous manipulation.
arXiv:2606. 04708v1 Announce Type: cross Abstract: Universal Manipulation Interface (UMI) enables scalable real-world robot data collection without hardware-specific teleoperation, yet leveraging UMI data to train large-scale Vision-Language-Action (VLA) models remains fundamentally challenging.
Sim-and-Human Co-training (SimHum) is a method that combines simulation and human demonstration data to train bimanual manipulation policies. It first extracts kinematic priors from simulation and visual priors from human observations, then fine‑tunes on a small real‑robot dataset. With only 80 real‑robot episodes per task, SimHum achieves 62.5% success on out‑of‑distribution scenes across four tabletop tasks, outperforming real‑only training by 53.7% and improving the best single‑source baseline by 35.0% in a matched‑time study.
DexPort is a portable interface that lets operators collect high‑quality dexterous hand demonstrations without a full robot setup. By using the same hand hardware for both collection and deployment, it records joint targets and states while allowing operators to specify wrist motion and select from a small set of coordinated finger skills. The authors collected over 2,500 demonstrations across four real‑world tasks, showing significant performance gains when using recorded robot commands and in‑the‑wild data.
arXiv:2606. 12936v1 Announce Type: cross Abstract: Wet-lab robots can improve the reproducibility, throughput, and safety of biomedical experiments, but scaling their learning requires customizable simulators for safe and reproducible task generation, open editable laboratory assets, and efficient pipelines that turn limited demonstrations into usable training data.
arXiv:2603. 03953v2 Announce Type: replace-cross Abstract: Safe visual navigation is critical for indoor mobile robots operating in cluttered environments.
Universal Manipulation Interface (UMI) enables scalable real-world robot data collection without hardware-specific teleoperation, yet leveraging UMI data to train large-scale Vision-Language-Action (VLA) models remains fundamentally challenging. We identify two critical mismatches: wrist-mounted fisheye views, with severe radial distortion and local gripper-centric perspectives, are out-of-distribution for pretrained VLMs; and human-collected trajectories frequently violate kinematic limits, incur collisions, or exceed controller bandwidth, teaching VLA policies physically infeasible actions.
arXiv:2510. 08807v2 Announce Type: replace-cross Abstract: From loco-motion to dextrous manipulation, humanoid robots have made remarkable strides in demonstrating complex full-body capabilities.
arXiv:2610.18950v1 Announce Type: cross Abstract: Humanoid loco-manipulation requires coordinated whole-body interaction with the physical world, yet collecting robot demonstrations at scale remains...