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cs.RO2026
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…
cs.RO2026
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…