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Found 4 papers, 1 papers with code
Bottom-Up Skill Discovery from Unsegmented Demonstrations for Long-Horizon Robot Manipulation
From the task structures of multi-task demonstrations, we identify skills based on the recurring patterns and train goal-conditioned sensorimotor policies with hierarchical imitation learning.
Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations
The experimental results in simulation and on the real robot have demonstrated that the use of implicit neural representations and joint learning of grasp affordance and 3D reconstruction have led to state-of-the-art grasping results.
Fast Uncertainty Quantification for Deep Object Pose Estimation
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 transfer.
Machine versus Human Attention in Deep Reinforcement Learning Tasks
1) How similar are the visual representations learned by RL agents and humans when performing the same task?
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