Decoupled Asynchronous System for Humanoids (DASH)
Overview
This paper introduces DASH, a Decoupled Asynchronous System for Humanoids designed to scale vision-language-action (VLA) models without compromising control latency. By splitting the policy into a slow vision-language backbone and a fast action head running concurrently via LCM, DASH maintains a stable 20 Hz control rate regardless of backbone size (tested from 0.8B to 8B parameters), whereas synchronous pipelines degrade significantly as model size increases. The system fuses frozen semantic information with fresh visual inputs and adapts to variable inference latency through desynchronized training and temporal aggregation. Evaluations on a simulated RoboTwin benchmark and a real humanoid robot with dexterous hands demonstrate that DASH sustains high control frequencies while achieving higher pick-and-place success rates compared to synchronous baselines.
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“frozen backbone with fresh vision from its own ResNet encoder, desynchronized training exposes it to the staleness it meets at deployment, and an adaptive temporal aggregator aligns overlapping action chunks under variable inference latency. On an asynchronous RoboTwin benchmark that charges inference latency in simulated time, DASH holds 20 Hz control rate from 0.8B to 8B backbones while the synchronous rate falls f”
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- arXiv Robotics — research abstractsSignalDecoupled Asynchronous System for Humanoids (DASH): Latency-Agnostic Scaling of Vision-Language-Action Models for Humanoid Manipulation
This paper introduces DASH, a Decoupled Asynchronous System for Humanoids designed to scale vision-language-action (VLA) models without compromising control latency. By splitting the policy into a slow vision-language backbone and a fast action head running concurrently via LCM, DASH maintains a stable 20 Hz control rate regardless of backbone size (tested from 0.8B to 8B parameters), whereas synchronous pipelines degrade significantly as model size increases. The system fuses frozen semantic information with fresh visual inputs and adapts to variable inference latency through desynchronized training and temporal aggregation. Evaluations on a simulated RoboTwin benchmark and a real humanoid robot with dexterous hands demonstrate that DASH sustains high control frequencies while achieving higher pick-and-place success rates compared to synchronous baselines.
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