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RoboSTD: A Bimanual Learning Method for Zero-Shot Single-to-Dual Transfer via Sagittal Mirroring

RoboSTD is a zero-shot single-to-dual data construction method for bimanual robot learning. It applies sagittal-plane mirroring to observations, proprioception, and actions to generate executable contralateral pseudo-demonstrations, then uses an LLM to generate structured spatio-temporal coordination constraints that guide the reconstruction of pseudo-bimanual supervision across workspace regions and task stages. The study evaluates RoboSTD on the AgileX ALOHA platform in RoboTwin simulation and real-world settings. Without additional bimanual demonstrations, it achieves an average of 2.11 times the number of cups collected by the single-arm baseline in the cup-collection task.

Part of the event: RoboSTD: Zero-Shot Construction of Dual-Arm Training Data from Single-Arm Data

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