161 citations · 1k across the 77 of their papers we have counts for
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Meta Reinforcement Learning for Sim-to-real Domain Adaptation
Karol Arndt, Murtaza Hazara, Ali Ghadirzadeh +1
Modern reinforcement learning methods suffer from low sample efficiency and unsafe exploration, making it infeasible to train robotic policies entirely on real hardware. In this wo…
Beyond Top-Grasps Through Scene Completion
Jens Lundell, Francesco Verdoja, Ville Kyrki
Current end-to-end grasp planning methods propose grasps in the order of seconds that attain high grasp success rates on a diverse set of objects, but often by constraining the wor…
Object Pose Estimation in Robotics Revisited
Antti Hietanen, Jyrki Latokartano, Alessandro Foi +4
Vision based object grasping and manipulation in robotics require accurate estimation of object's 6D pose. The 6D pose estimation has received significant attention in computer vis…