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20112023
most citedGaussian Processes for Data-Efficient Learning in Robotics and Control

667 citations · 2.8k across the 127 of their papers we have counts for

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Showing 2018Show all

11 papers · 1 filter

cs.CV2018★ 6 cited

Object Discovery in Videos as Foreground Motion Clustering

Christopher Xie, Yu Xiang, Zaid Harchaoui +1

We consider the problem of providing dense segmentation masks for object discovery in videos. We formulate the object discovery problem as foreground motion clustering, where the g…

cs.CV2018

Early Fusion for Goal Directed Robotic Vision

Aaron Walsman, Yonatan Bisk, Saadia Gabriel +4

Building perceptual systems for robotics which perform well under tight computational budgets requires novel architectures which rethink the traditional computer vision pipeline. M…

cs.RO2018★ 24 cited

RMPflow: A Computational Graph for Automatic Motion Policy Generation

Ching-An Cheng, Mustafa Mukadam, Jan Issac +4

We develop a novel policy synthesis algorithm, RMPflow, based on geometrically consistent transformations of Riemannian Motion Policies (RMPs). RMPs are a class of reactive motion…

cs.RO2018★ 20 cited

Learning Latent Space Dynamics for Tactile Servoing

Giovanni Sutanto, Nathan Ratliff, Balakumar Sundaralingam +4

To achieve a dexterous robotic manipulation, we need to endow our robot with tactile feedback capability, i.e. the ability to drive action based on tactile sensing. In this paper,…

cs.RO2018★ 17 cited

GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learning

Jacky Liang, Viktor Makoviychuk, Ankur Handa +3

Most Deep Reinforcement Learning (Deep RL) algorithms require a prohibitively large number of training samples for learning complex tasks. Many recent works on speeding up Deep RL…

cs.RO2018

Robust Learning of Tactile Force Estimation through Robot Interaction

Balakumar Sundaralingam, Alexander Lambert, Ankur Handa +5

Current methods for estimating force from tactile sensor signals are either inaccurate analytic models or task-specific learned models. In this paper, we explore learning a robust…