Robot manipulation — grasping, placing, and using tools — is where sim-to-real and foundation models meet physical dexterity. DexNet (2017) pioneered data-driven grasp planning, but the field accelerated when contact-rich manipulation was tackled with RL in simulation (DexterousHands, 2023) and then transferred to real hardware. Current state-of-the-art combines diffusion policies (Chi et al., 2023) with large pretrained vision encoders to achieve robust 6-DOF manipulation from a handful of demonstrations, though deformable objects and multi-step assembly remain unsolved.
LIBERO-Long (also called LIBERO-10) is one of four task suites in the LIBERO benchmark for lifelong robot learning. It contains 10 long-horizon manipulation tasks requiring multi-step reasoning and diverse object/spatial/goal knowledge. Reported as success rate (%).
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