Hugging Face Trending Papers
UMI-Live for In-The-Wild Bimanual Robotics Data Collection
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
arXiv AI
6d ago
UMI-Live for In-The-Wild Bimanual Robotics Data Collection
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.
arXiv AI
Oct 7
VOMMI: Collecting and Leveraging Portable Demonstrations for Mobile Manipulation
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...
arXiv AI
Jun 10
YUBI: Yielding Universal Bidigital Interface for Bimanual Dexterous Manipulation at Scale
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 AI
Sep 18
HIL-UMI: Bringing Human-in-the-Loop Post-Training of Vision-Language-Action Models to Universal Manipulation Interface
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 Machine Learning
Jun 19
Humanoid Everyday: A Comprehensive Robotic Dataset for Open-World Humanoid Manipulation
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 AI
Sep 16
HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos
HumanEgo is a framework that enables zero‑shot robot learning from short egocentric human videos by converting each demonstration into an entity‑level hand‑object interaction representation and training a flow‑matching policy with dense auxiliary objectives. The method is robot‑data‑free, hardware‑agnostic, and data‑efficient, achieving 92.5 % success on four real‑world tasks with only 30 minutes of human video per task and outperforming matched‑time robot teleoperation by 41 %. HumanEgo also robustly transfers zero‑shot across new robots, cameras, and environments, and is released as an open‑source tool for learning robot policies directly from human data.