Towards Synergistic, Generalized, and Efficient Dual-System for Robotic Manipulation
Keywords: Robotic Manipulation, Vision-Language-Action Models
TL;DR: We propose a synergistic dual-system framework that leverages the strengths of both generalist and specialist policy and paves the path to the practical deployment of VLA models.
Abstract: The increasing demand for versatile robotic systems to operate in diverse and dynamic environments has emphasized the importance of a generalist policy, which leverages a large cross-embodiment data corpus to facilitate broad adaptability and high-level reasoning. However, the generalist would struggle with inefficient inference and cost-expensive training. The specialist policy, instead, is curated for specific domain data and excels at task-level precision with efficiency. Yet, it lacks the generalization capacity for a wide range of applications. Inspired by these observations, we introduce RoboDual, a synergistic dual-system that supplements the merits of both generalist and specialist policy. A diffusion transformer-based specialist is devised for multi-step action rollouts, exquisitely conditioned on the high-level task understanding and discretized action output of a vision-language-action (VLA) based generalist. Compared to OpenVLA, RoboDual achieves 26.7% improvement in real-world setting and 12% gain on CALVIN by introducing a specialist policy with merely 20M trainable parameters. It maintains strong performance with 5% of demonstration data only, and enables a 3.8$\times$ higher control frequency in real-world deployment. Code would be made publicly available.
Primary Area: applications to robotics, autonomy, planning
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Submission Number: 664
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