💡 The paper presents LingBot-VLA 2.0, an improved version of the VLA foundation model, which aims to bridge the gap between laboratory conditions and real-world applications. The main problem addressed is the disparity between the two environments, which hinders the practical implementation of VLA models. To solve this, the authors propose three main improvements.

First, they enhance generalization across tasks and embodiments by expanding the data preprocessing pipeline and training the model on a large dataset of around 60,000 hours of data, including 50,000 hours of robot trajectories from 20 robot configurations and 10,000 hours of human videos.

Second, they extend the action space to include whole-body degrees of freedom, allowing the robots to perform more complex tasks. This is achieved by accommodating degrees of freedom for the heads, waists, mobile bases, and dexterous hands, in addition to dual-arm hardware platforms.

Third, they incorporate predictive dynamics modeling for improved temporal reasoning. This is done by formulating future prediction as a proxy task, using a video representation model for semantic priors and a depth estimation model for geometric cues.

The results show that these modifications have a beneficial impact, as evaluated on the GM-100 benchmark in a generalist setting. Additionally, the expanded pretraining data enables LingBot-VLA 2.0 to demonstrate strong cross-embodiment long-horizon mobile manipulation capability across two robotic platforms. Overall, the paper presents significant improvements to the VLA foundation model, making it more suitable for real-world applications.