CARE：用于机器人控制的潜在连续动作表示的多任务预训练
CARE: Multi-Task Pretraining for Latent Continuous Action Representation in Robot Control
AI总结：
CARE通过多任务预训练学习连续动作表示，提升机器人控制的可扩展性和可解释性。
摘要：
最近在视觉-语言-动作（VLA）模型方面的进展在机器人控制中显示出潜力，但其对动作监督的依赖限制了可扩展性和泛化能力。为了解决这一挑战，我们引入了CARE，一种新的框架，旨在为机器人任务执行训练VLA模型。与现有方法在预训练过程中依赖动作注释不同，CARE通过仅利用视频-文本对消除了对显式动作标签的需要。这些弱对齐的数据源使模型能够通过新设计的多任务预训练目标学习连续潜在动作表示。在微调过程中，使用少量标注数据训练动作头以进行控制。在各种模拟任务中的实验结果表明，CARE在成功率、语义可解释性和避免捷径学习方面均优于其他方法。这些结果突显了CARE在弱监督下的可扩展性、可解释性和在机器人控制中的有效性。
英文摘要：
Recent advances in Vision-Language-Action (VLA) models have shown promise for robot control, but their dependence on action supervision limits scalability and generalization. To address this challenge, we introduce CARE, a novel framework designed to train VLA models for robotic task execution. Unlike existing methods that depend on action annotations during pretraining, CARE eliminates the need for explicit action labels by leveraging only video-text pairs. These weakly aligned data sources enable the model to learn continuous latent action representations through a newly designed multi-task pretraining objective. During fine-tuning, a small set of labeled data is used to train the action head for control. Experimental results across various simulation tasks demonstrate CARE's superior success rate, semantic interpretability, and ability to avoid shortcut learning. These results underscore CARE's scalability, interpretability, and effectiveness in robotic control with weak supervision.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
