AffordSim: A Scalable Data Generator and Benchmark for Affordance-Aware Robotic Manipulation

arXiv:2604.11674v1 Announce Type: cross Abstract: Simulation-based data generation has become a dominant paradigm for training robotic manipulation policies, yet existing platforms do not incorporate object affordance information into trajectory generation. As a result, tasks requiring precise interaction with specific functional regions--grasping a mug by its handle, pouring from a cup's rim, or hanging a mug on a hook--cannot be automatically generated with semantically correct trajectories. W