Robot Manipulation

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.

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Datasets
3
Results
success_rate
Canonical metric
Canonical Benchmark

LIBERO-Long

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 (%).

Primary metric: success_rate
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Top 10

Leading models on LIBERO-Long.

RankModelsuccess_rateYearSource
1
π0 (Pi-Zero)
85.22026paper
2
OpenVLA
53.72026paper
3
Octo-Base
51.12026paper

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