RTPolicy: Real-Time (32.9 Hz) Multi-View Visuomotor Policy for Robotic Manipulation on Edge Devices
Overview
This research introduces RTPolicy, a multi-view visuomotor policy optimized for low-latency robotic manipulation on edge devices. By combining view-aware feature fusion with visual compression and a flow-matching action generator, the system achieves real-time inference at 32.9 Hz (30.35 ms latency) on an RDK S600 device while maintaining high performance. On the RoboTwin~2.0 benchmark across 50 tasks, RTPolicy achieves a mean success rate of 52.26%, outperforming the strongest baseline ($\pi_0$ at 46.4%). Under asynchronous streaming inference on a real bimanual platform, it sustains a 72.0% mean success rate. The method reduces computational costs for visual representation and action generation while preserving necessary visual information.
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Reported quantity · inference latency: 30.35 other · Basis not reported · Differing source assertions
“reaming inference on a real bimanual platform, where RTPolicy attains a mean success rate of 72.0\% and sustains real-time inference at 30.35 ms (32.9 Hz) on the RDK S600 and 149.72 ms (6.7 Hz) on the RDK S100, both in INT16. Project Page: https://d-robotics-ai-lab.github.io/RTPolicy.project.”
Exact source · revision 1Source owner not reported
Reported quantity · inference latency: 149.72 other · Basis not reported · Differing source assertions
“reaming inference on a real bimanual platform, where RTPolicy attains a mean success rate of 72.0\% and sustains real-time inference at 30.35 ms (32.9 Hz) on the RDK S600 and 149.72 ms (6.7 Hz) on the RDK S100, both in INT16. Project Page: https://d-robotics-ai-lab.github.io/RTPolicy.project.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalRTPolicy: Real-Time (32.9 Hz) Multi-View Visuomotor Policy for Robotic Manipulation on Edge Devices
This research introduces RTPolicy, a multi-view visuomotor policy optimized for low-latency robotic manipulation on edge devices. By combining view-aware feature fusion with visual compression and a flow-matching action generator, the system achieves real-time inference at 32.9 Hz (30.35 ms latency) on an RDK S600 device while maintaining high performance. On the RoboTwin~2.0 benchmark across 50 tasks, RTPolicy achieves a mean success rate of 52.26%, outperforming the strongest baseline ($\pi_0$ at 46.4%). Under asynchronous streaming inference on a real bimanual platform, it sustains a 72.0% mean success rate. The method reduces computational costs for visual representation and action generation while preserving necessary visual information.
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