2 citations · 2 across the 5 of their papers we have counts for
6 papers
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…
Towards Trustworthy Physical Intelligence: From Theory to Practice Across Life Cycle
Wang Yang, Yang Wang, Hongxuan Liu +45
Physical intelligence refers to intelligence systems that understand, reason about, and act in accordance with the physical world and its underlying laws, dynamics, and constraints…
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…