3 citations · 6 across the 10 of their papers we have counts for
11 papers
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…
Touch begins where vision ends: Generalizable policies for contact-rich manipulation
Zifan Zhao, Siddhant Haldar, Jinda Cui +2
Data-driven approaches struggle with precise manipulation; imitation learning requires many hard-to-obtain demonstrations, while reinforcement learning yields brittle, non-generali…
eFlesh: Highly customizable Magnetic Touch Sensing using Cut-Cell Microstructures
Venkatesh Pattabiraman, Zizhou Huang, Daniele Panozzo +3
If human experience is any guide, operating effectively in unstructured environments -- like homes and offices -- requires robots to sense the forces during physical interaction. Y…
EgoZero: Robot Learning from Smart Glasses
Vincent Liu, Ademi Adeniji, Haotian Zhan +4
Despite recent progress in general purpose robotics, robot policies still lag far behind basic human capabilities in the real world. Humans interact constantly with the physical wo…
Feel the Force: Contact-Driven Learning from Humans
Ademi Adeniji, Zhuoran Chen, Vincent Liu +5
Controlling fine-grained forces during manipulation remains a core challenge in robotics. While robot policies learned from robot-collected data or simulation show promise, they st…
RUKA: Rethinking the Design of Humanoid Hands with Learning
Anya Zorin, Irmak Guzey, Billy Yan +4
Dexterous manipulation is a fundamental capability for robotic systems, yet progress has been limited by hardware trade-offs between precision, compactness, strength, and affordabi…
Bridging the Human to Robot Dexterity Gap through Object-Oriented Rewards
Irmak Guzey, Yinlong Dai, Georgy Savva +2
Training robots directly from human videos is an emerging area in robotics and computer vision. While there has been notable progress with two-fingered grippers, learning autonomou…
Learning Precise, Contact-Rich Manipulation through Uncalibrated Tactile Skins
Venkatesh Pattabiraman, Yifeng Cao, Siddhant Haldar +2
While visuomotor policy learning has advanced robotic manipulation, precisely executing contact-rich tasks remains challenging due to the limitations of vision in reasoning about p…
Neural Circuit Architectural Priors for Quadruped Locomotion
Nikhil X. Bhattasali, Venkatesh Pattabiraman, Lerrel Pinto +1
Learning-based approaches to quadruped locomotion commonly adopt generic policy architectures like fully connected MLPs. As such architectures contain few inductive biases, it is c…
AnySkin: Plug-and-play Skin Sensing for Robotic Touch
Raunaq Bhirangi, Venkatesh Pattabiraman, Enes Erciyes +3
While tactile sensing is widely accepted as an important and useful sensing modality, its use pales in comparison to other sensory modalities like vision and proprioception. AnySki…
Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Prediction
Chen-Yu Yen, Raghav Singhal, Umang Sharma +3
Magnetic Resonance (MR) imaging, despite its proven diagnostic utility, remains an inaccessible imaging modality for disease surveillance at the population level. A major factor re…