1 citations · 2 across the 12 of their papers we have counts for
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GORDON: Graph-based Object-centric Rewards for Decomposition of Long-Horizon Manipulation
Andrea Protopapa, Davide Buoso, Francesca Pistilli +2
Learning long-horizon manipulation skills with reinforcement learning remains challenging due to the complexity of reward design, the limited guidance of sparse rewards, and the hi…
GAP: Geometric Anchor Pre-training for Data-Efficient Visuomotor Learning of Manipulation Tasks
Davide Buoso, Andrea Protopapa, Stefano Di Carlo +2
Learning visuomotor policies from scarce expert demonstrations remains a core challenge in robotic manipulation. A primary hurdle lies in distilling high-dimensional RGB representa…
Graph learning in robotics: a survey
Francesca Pistilli, Giuseppe Averta
Deep neural networks for graphs have emerged as a powerful tool for learning on complex non-euclidean data, which is becoming increasingly common for a variety of different applica…