[18 October, 11:00] Let's Talk ML

Radek Bartyzal - HOP-Rec: High-Order Proximity for Implicit Recommendation (pdf) (slides)

Two of the most popular approaches to recommender systems are based on factorization and graph-based models. This recent paper introduces a method combining both of these approaches.

Ondra Bíža - Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning (pdf) (slides)

Skilled robotic manipulation benefits from complex synergies between pushing and grasping actions: pushing can help rearrange cluttered objects to make space for arms and fingers; likewise, grasping can help displace objects to make pushing movements more precise and collision-free. This paper presents a policy able to learn pushing motions that enable future grasps, while learning grasps that can leverage past pushes.

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