activity
20212026
most citedA Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation

2 citations · 2 across the 2 of their papers we have counts for

collaborators
Showing cs.ROShow all

5 papers · 1 filter

cs.RO2026

A Systematic Study of Data Modalities and Strategies for Co-training Large Behavior Models for Robot Manipulation

Fanqi Lin, Kushal Arora, Jean Mercat +9

Large behavior models have shown strong dexterous manipulation capabilities by extending imitation learning to large-scale training on multi-task robot data, yet their generalizati…

cs.RO20252 cited

A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation

TRI LBM Team, Jose Barreiros, Andrew Beaulieu +79

Robot manipulation has seen tremendous progress in recent years, with imitation learning policies enabling successful performance of dexterous and hard-to-model tasks. Concurrently…

cs.RO2023

Proximity and Visuotactile Point Cloud Fusion for Contact Patches in Extreme Deformation

Jessica Yin, Paarth Shah, Naveen Kuppuswamy +5

Visuotactile sensors are a popular tactile sensing strategy due to high-fidelity estimates of local object geometry. However, existing algorithms for processing raw sensor inputs t…

cs.RO2021

Variable compliance and geometry regulation of Soft-Bubble grippers with active pressure control

Sihah Joonhigh, Naveen Kuppuswamy, Andrew Beaulieu +2

While compliant grippers have become increasingly commonplace in robot manipulation, finding the right stiffness and geometry for grasping the widest variety of objects remains a k…

cs.RO2021

Monocular Depth Estimation for Soft Visuotactile Sensors

Rares Ambrus, Vitor Guizilini, Naveen Kuppuswamy +3

Fluid-filled soft visuotactile sensors such as the Soft-bubbles alleviate key challenges for robust manipulation, as they enable reliable grasps along with the ability to obtain hi…