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R2RGEN: Real-to-Real 3D Data Generation for Spatially Generalized Manipulation

Description

Towards the aim of generalized robotic manipulation, spatial generalization is the most fundamental capability that requires the policy to work robustly under different spatial distribution of objects, environment and agent itself. To achieve this, substantial human demonstrations need to be collected to cover different spatial configurations for training a generalized visuomotor policy via imitation learning. Prior works explore a promising direction that leverages data generation to acquire ab

Source

http://arxiv.org/abs/2510.08547v1