activity
20162022
most citedMESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance

5 citations · 8 across the 5 of their papers we have counts for

collaborators

5 papers

cs.RO2022

Mechanical Search on Shelves using a Novel "Bluction" Tool

Huang Huang, Michael Danielczuk, Chung Min Kim +6

Shelves are common in homes, warehouses, and commercial settings due to their storage efficiency. However, this efficiency comes at the cost of reduced visibility and accessibility…

cs.RO2021

Learning to Localize, Grasp, and Hand Over Unmodified Surgical Needles

Albert Wilcox, Justin Kerr, Brijen Thananjeyan +5

Robotic Surgical Assistants (RSAs) are commonly used to perform minimally invasive surgeries by expert surgeons. However, long procedures filled with tedious and repetitive tasks s…

cs.LG20215 cited

MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance

Michael Luo, Ashwin Balakrishna, Brijen Thananjeyan +6

Safe exploration is critical for using reinforcement learning (RL) in risk-sensitive environments. Recent work learns risk measures which measure the probability of violating const…

cs.RO20211 cited

FogROS: An Adaptive Framework for Automating Fog Robotics Deployment

Kaiyuan, Chen, Yafei Liang +6

As many robot automation applications increasingly rely on multi-core processing or deep-learning models, cloud computing is becoming an attractive and economically viable resource…

cs.RO20162 cited

Comparing Human-Centric and Robot-Centric Sampling for Robot Deep Learning from Demonstrations

Michael Laskey, Caleb Chuck, Jonathan Lee +5

Motivated by recent advances in Deep Learning for robot control, this paper considers two learning algorithms in terms of how they acquire demonstrations. "Human-Centric" (HC) samp…