TwinBridge: Digital Twin-Driven Policy and Reward Transfer for Real-World Reinforcement Learning in Laboratory Robotics
Overview
This paper introduces TwinBridge, a digital twin-driven framework that enables online reinforcement learning for precise laboratory manipulation. By combining 3D Gaussian splatting with physics simulation, the system constructs digital twins to provide policy initialization and dense visual rewards. The framework distills supervision into a visual reward model used during real-world interaction to compute dense rewards for human-in-the-loop policy refinement. Experiments across five real-world laboratory tasks show that TwinBridge achieves a final mean autonomous success rate of 98% with at most 25 minutes of real-world interaction per task, demonstrating that dense rewards improve efficiency over sparse rewards and simulation-based initialization accelerates learning.
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Environment: controlled real
“on history to compute dense rewards for human-in-the-loop policy refinement. Across five real-world laboratory tasks, TwinBridge achieves a final mean autonomous success rate of 98% with at most 25 minutes of real-world interaction per task. Experiments show that dense rewards improve interaction efficiency over sparse rewards, and simulation-based policy initialization further accelerates learning.”
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Control: reported autonomous
“on history to compute dense rewards for human-in-the-loop policy refinement. Across five real-world laboratory tasks, TwinBridge achieves a final mean autonomous success rate of 98% with at most 25 minutes of real-world interaction per task. Experiments show that dense rewards improve interaction efficiency over sparse rewards, and simulation-based policy initialization further accelerates learning.”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 98 percent · aggregate sensor hours
“on history to compute dense rewards for human-in-the-loop policy refinement. Across five real-world laboratory tasks, TwinBridge achieves a final mean autonomous success rate of 98% with at most 25 minutes of real-world interaction per task. Experiments show that dense rewards improve interaction efficiency over sparse rewards, and simulation-based policy initialization further accelerates learning.”
Exact source · revision 1Source owner not reported
Reported quantity · Reported duration: 25 other · Basis not reported
“on history to compute dense rewards for human-in-the-loop policy refinement. Across five real-world laboratory tasks, TwinBridge achieves a final mean autonomous success rate of 98% with at most 25 minutes of real-world interaction per task. Experiments show that dense rewards improve interaction efficiency over sparse rewards, and simulation-based policy initialization further accelerates learning.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalTwinBridge: Digital Twin-Driven Policy and Reward Transfer for Real-World Reinforcement Learning in Laboratory Robotics
This paper introduces TwinBridge, a digital twin-driven framework that enables online reinforcement learning for precise laboratory manipulation. By combining 3D Gaussian splatting with physics simulation, the system constructs digital twins to provide policy initialization and dense visual rewards. The framework distills supervision into a visual reward model used during real-world interaction to compute dense rewards for human-in-the-loop policy refinement. Experiments across five real-world laboratory tasks show that TwinBridge achieves a final mean autonomous success rate of 98% with at most 25 minutes of real-world interaction per task, demonstrating that dense rewards improve efficiency over sparse rewards and simulation-based initialization accelerates learning.
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