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MaskVLA, a masking-based fine-tuning strategy that randomly masking a small portion of the main camera's visual information leads to the emergence of robust policies, thereby enhancing the model's capability to tackle complex manipulation tasks and improving its generalization performance.
This work aims to advance the development of socially desirable embodied agents in open-domain environments by extending the concept of proactive assistance to Social Proactive Intelligence (SPI), characterized by diverse scenarios, social understanding, and robot-centric behaviors.
This letter presents PrismAD, a decoupled end-to-end autonomous driving framework based on a Semantic Mixture-of-Planners that exhibits competitive performance on the nuScenes open-loop dataset and NeuroNCAP closed-loop benchmark.
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