2 citations · 2 across the 5 of their papers we have counts for
5 papers · 1 filter
Steertuning Policies from a Few Human Videos
Billy Yan, Isaac Tu, Kelly Lee +8
Current recipes for robot learning require massive amounts of robot demonstrations to obtain robust and adaptive policies. In this work, we introduce Steertuning, a system that ena…
Human Universal Grasping
Kevin Yuanbo Wu, Tianxing Zhou, Isaac Tu +5
Humans can grasp objects effortlessly, whereas multi-fingered robots are far from this level of generality. We argue that the most natural source of robot grasping data is from hum…
Dexterity from Smart Lenses: Multi-Fingered Robot Manipulation with In-the-Wild Human Demonstrations
Irmak Guzey, Haozhi Qi, Julen Urain +10
Learning multi-fingered robot policies from humans performing daily tasks in natural environments has long been a grand goal in the robotics community. Achieving this would mark si…
See to Touch: Learning Tactile Dexterity through Visual Incentives
Irmak Guzey, Yinlong Dai, Ben Evans +2
Equipping multi-fingered robots with tactile sensing is crucial for achieving the precise, contact-rich, and dexterous manipulation that humans excel at. However, relying solely on…
Holo-Dex: Teaching Dexterity with Immersive Mixed Reality
Sridhar Pandian Arunachalam, Irmak Güzey, Soumith Chintala +1
A fundamental challenge in teaching robots is to provide an effective interface for human teachers to demonstrate useful skills to a robot. This challenge is exacerbated in dextero…