4 citations · 4 across the 4 of their papers we have counts for
7 papers
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…
Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning
Michelle A. Lee, Carlos Florensa, Jonathan Tremblay +4
Traditional robotic approaches rely on an accurate model of the environment, a detailed description of how to perform the task, and a robust perception system to keep track of the…
PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data
Zheng Tang, Milind Naphade, Stan Birchfield +5
In comparison with person re-identification (ReID), which has been widely studied in the research community, vehicle ReID has received less attention. Vehicle ReID is challenging d…
Contextual Reinforcement Learning of Visuo-tactile Multi-fingered Grasping Policies
Visak Kumar, Tucker Hermans, Dieter Fox +2
Using simulation to train robot manipulation policies holds the promise of an almost unlimited amount of training data, generated safely out of harm's way. One of the key challenge…
Camera-to-Robot Pose Estimation from a Single Image
Timothy E. Lee, Jonathan Tremblay, Thang To +5
We present an approach for estimating the pose of an external camera with respect to a robot using a single RGB image of the robot. The image is processed by a deep neural network…
Toward Sim-to-Real Directional Semantic Grasping
Shariq Iqbal, Jonathan Tremblay, Thang To +6
We address the problem of directional semantic grasping, that is, grasping a specific object from a specific direction. We approach the problem using deep reinforcement learning vi…