arXiv:2607. 19479v1 Announce Type: cross Abstract: Existing teleoperation systems are often tailored to specific robot hardware and task domains, limiting their scalability and adaptability.
arXiv:2606. 14561v1 Announce Type: cross Abstract: Robotics manipulation research increasingly focuses on two-finger parallel grippers for their effectiveness, affordability, and ease of teleoperation.
arXiv:2606. 14218v1 Announce Type: cross Abstract: For robots to work safely in household environments, they need to be compliant and react to torque and force feedback during contact.
arXiv:2602. 05513v3 Announce Type: replace-cross Abstract: Bimanual dexterous manipulation relies on integrating multimodal inputs to perform complex real-world tasks.
arXiv:2607. 20399v1 Announce Type: cross Abstract: Full-sized humanoid robot capabilities have grown exponentially in recent years, aiming towards general-purpose deployment in human environments.
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:2608.29601v2 Announce Type: replace-cross Abstract: We present $N_0$-Foundation, a paradigm for tactile-enabled embodied manipulation, which integrates tactile sensing hardware, large-scale mul...
arXiv:2609.34182v2 Announce Type: replace-cross Abstract: Dexterous manipulation requires tactile feedback. However, robot tactile demonstrations are difficult to scale,because dexterous-hand teleope...
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:2505. 04999v2 Announce Type: replace-cross Abstract: Learning robot control policies from demonstrations typically requires action-labeled expert data, which is expensive to collect through teleoperation.
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.
arXiv:2609.21449v1 Announce Type: new Abstract: World Action Models bring the predictive capabilities of video models into robot action generation, providing a rich foundation for modeling future vis...
arXiv:2608.22296v1 Announce Type: cross Abstract: Quadruped mobile manipulation requires two tightly coupled capabilities: reaching manipulation-ready configurations and maintaining stable contact th...