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20172022
most citedEfficient Hierarchical Graph-Based Segmentation of RGBD Videos

52 citations · 158 across the 17 of their papers we have counts for

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12 papers · 1 filter

cs.RO2021

Multi-View Fusion for Multi-Level Robotic Scene Understanding

Yunzhi Lin, Jonathan Tremblay, Stephen Tyree +2

We present a system for multi-level scene awareness for robotic manipulation. Given a sequence of camera-in-hand RGB images, the system calculates three types of information: 1) a…

cs.RO202013 cited

Hierarchical Planning for Long-Horizon Manipulation with Geometric and Symbolic Scene Graphs

Yifeng Zhu, Jonathan Tremblay, Stan Birchfield +1

We present a visually grounded hierarchical planning algorithm for long-horizon manipulation tasks. Our algorithm offers a joint framework of neuro-symbolic task planning and low-l…

cs.RO20202 cited

Fast Uncertainty Quantification for Deep Object Pose Estimation

Guanya Shi, Yifeng Zhu, Jonathan Tremblay +4

Deep learning-based object pose estimators are often unreliable and overconfident especially when the input image is outside the training domain, for instance, with sim2real transf…

cs.RO2020

Joint Space Control via Deep Reinforcement Learning

Visak Kumar, David Hoeller, Balakumar Sundaralingam +2

The dominant way to control a robot manipulator uses hand-crafted differential equations leveraging some form of inverse kinematics / dynamics. We propose a simple, versatile joint…

cs.RO2020

Indirect Object-to-Robot Pose Estimation from an External Monocular RGB Camera

Jonathan Tremblay, Stephen Tyree, Terry Mosier +1

We present a robotic grasping system that uses a single external monocular RGB camera as input. The object-to-robot pose is computed indirectly by combining the output of two neura…

cs.RO20205 cited

RMPflow: A Geometric Framework for Generation of Multi-Task Motion Policies

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

Generating robot motion for multiple tasks in dynamic environments is challenging, requiring an algorithm to respond reactively while accounting for complex nonlinear relationships…