Multimodal pretraining has emerged as an effective strategy for the trinity of goals of representation learning in autonomous robots: 1) extracting both local and global task progression information; 2) enforcing temporal consistency of visual representation; 3) capturing trajectory-level language grounding. Most existing methods approach these via separate objectives, which often reach sub-optimal solutions. In this paper, we propose a universal unified objective that can simultaneously extract meaningful task progression information from image sequences and seamlessly align them with language instructions. We discover that via implicit preferences, where a visual trajectory inherently aligns better with its corresponding language instruction than mismatched pairs, the popular Bradley-Terry model can transform into representation learning through proper reward reparameterizations. The resulted framework, DecisionNCE, mirrors an InfoNCE-style objective but is distinctively tailored for decision-making tasks, providing an embodied representation learning framework that elegantly extracts both local and global task progression features, with temporal consistency enforced through implicit time contrastive learning, while ensuring trajectory-level instruction grounding via multimodal joint encoding. Evaluation on both simulated and real robots demonstrates that DecisionNCE effectively facilitates diverse downstream policy learning tasks, offering a versatile solution for unified representation and reward learning. Project Page: https://2toinf.github.io/DecisionNCE/

翻译：多模态预训练已成为自主机器人表征学习三重目标的有效策略：1）提取局部与全局任务进展信息；2）强化视觉表征的时间一致性；3）捕获轨迹级别的语言对齐。现有方法大多通过分离式目标实现上述功能，往往只能获得次优解。本文提出一种通用统一目标，可同时从图像序列中提取有意义的任务进展信息，并将其与语言指令无缝对齐。我们发现，通过隐式偏好（即视觉轨迹与其对应语言指令具有天然优于错配对的内在一致性），Bradley-Terry模型可通过适当的奖励重参数化转化为表征学习。所提出的DecisionNCE框架与InfoNCE风格目标函数相似，但专为决策任务量身定制——通过隐式时间对比学习强化时间一致性，同时借助多模态联合编码确保轨迹级指令对齐，从而优雅地提取局部与全局任务进展特征。在仿真与真实机器人上的评估表明，DecisionNCE能有效促进多样化的下游策略学习任务，为统一表征与奖励学习提供通用解决方案。项目页面：https://2toinf.github.io/DecisionNCE/