ConLA：从人类视频中通过对比学习学习机器人操作
ConLA: Contrastive Latent Action Learning from Human Videos for Robotic Manipulation
AI总结：
ConLA通过对比学习从人类视频中学习机器人操作，有效解决潜在表示中的捷径学习问题，提升机器人学习的可扩展性。
摘要：
视觉-语言-动作（VLA）模型通过在大规模机器人远程操作数据集上进行预训练来实现初步的泛化。然而，获取涵盖多样任务和环境的全面数据集非常昂贵且难以扩展。相比之下，人类示范视频提供了一个丰富且可扩展的场景和操作行为来源，但其缺乏显式的动作监督阻碍了直接利用。先前的工作利用基于VQ-VAE的框架，以无监督的方式从人类视频中学习潜在动作。然而，由于训练目标主要集中在重建视觉外观而不是捕捉帧间动态，学习到的表示往往依赖于虚假的视觉线索，导致捷径学习和纠缠的潜在表示，阻碍了可转移性。为了解决这个问题，我们提出了ConLA，一个用于从人类视频中学习机器人策略的无监督预训练框架。ConLA引入了一种对比解纠缠机制，利用动作类别先验和时间线索来分离运动动态和视觉内容，有效地缓解了捷径学习。广泛的实验表明，ConLA在多样化的基准上实现了强劲的性能。值得注意的是，通过仅在人类视频上预训练，我们的方法首次超越了使用真实机器人轨迹预训练所获得的性能，突显了其提取纯且语义一致的潜在动作表示的能力，为可扩展的机器人学习提供了可能。
英文摘要：
Vision-Language-Action (VLA) models achieve preliminary generalization through pretraining on large scale robot teleoperation datasets. However, acquiring datasets that comprehensively cover diverse tasks and environments is extremely costly and difficult to scale. In contrast, human demonstration videos offer a rich and scalable source of diverse scenes and manipulation behaviors, yet their lack of explicit action supervision hinders direct utilization. Prior work leverages VQ-VAE based frameworks to learn latent actions from human videos in an unsupervised manner. Nevertheless, since the training objective primarily focuses on reconstructing visual appearances rather than capturing inter-frame dynamics, the learned representations tend to rely on spurious visual cues, leading to shortcut learning and entangled latent representations that hinder transferability. To address this, we propose ConLA, an unsupervised pretraining framework for learning robotic policies from human videos. ConLA introduces a contrastive disentanglement mechanism that leverages action category priors and temporal cues to isolate motion dynamics from visual content, effectively mitigating shortcut learning. Extensive experiments show that ConLA achieves strong performance across diverse benchmarks. Notably, by pretraining solely on human videos, our method for the first time surpasses the performance obtained with real robot trajectory pretraining, highlighting its ability to extract pure and semantically consistent latent action representations for scalable robot learning.
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。
