Towards Synergistic, Generalized, and Efficient Dual-System for Robotic
  Manipulation
Authors: name: Qingwen Bu, name: Hongyang Li, name: Li Chen, name: Jisong Cai, name: Jia Zeng, name: Heming Cui, name: Maoqing Yao, name: Yu Qiao

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. Our project page
is hosted at: https://opendrivelab.com/RoboDual/