Real-World Large-Scale Test Shows Robots Learning While Deploying Outperform Baselines on Eight Tasks

The article presents the LWD (Learning While Deploying) framework, detailing its reinforcement‑learning‑driven data flywheel, the DIVL value‑evaluation and QAM policy‑optimization modules, and experimental results where a dual‑arm robot improves success rates by up to 17% and reduces cycle time by 23.75 seconds across eight real‑world tasks, surpassing strong baselines.

DIVLData FlywheelLWD

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Real-World Large-Scale Test Shows Robots Learning While Deploying Outperform Baselines on Eight Tasks