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Vision-Language-Action Model Robot Learning Framework for Agricultural Manipulation

A research team proposed a simulation-to-real robot learning framework for agricultural reaching and grasping, building a MuJoCo-based agricultural environment with diverse tomato plant structures and collecting expert demonstrations via joystick teleoperation. Two pretrained generalist policies, Octo and π0.5, were first adapted with simulated demonstrations, then fine-tuned with real demonstrations on a Franka Research 3 robotic arm. Results showed that π0.5 scored higher than Octo on task performance and degraded less under distribution shifts such as viewpoint and spatial offsets; the study also examined the effect of proprioceptive information on policy performance.

Part of the event: Sim-to-real framework proposed for agricultural grasping VLA

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