DexWild：面向真实场景的机器人策略的灵巧交互
DexWild: Dexterous Human Interactions for In-the-Wild Robot Policies
AI总结：
本文提出DexWild框架，通过结合人类和机器人示范数据，提升机器人在多样化环境中的泛化能力，实验表明其在未见环境中的成功率显著高于传统方法。
摘要：
大规模、多样化的机器人数据集已成为使灵巧操作策略泛化到新环境的有希望途径，但获取此类数据集存在诸多挑战。虽然远程操作能提供高保真的数据集，但其高成本限制了可扩展性。相反，如果人们可以像在日常生活中一样使用自己的手来收集数据呢？在DexWild中，一个多样化的数据收集团队使用他们的手在多种环境和物体上收集数小时的交互数据。为了记录这些数据，我们创建了DexWild-System，一种低成本、移动且易于使用的设备。DexWild学习框架在人类和机器人示范数据上共同训练，相较于单独训练每个数据集，其性能得到提升。这种组合产生了能够泛化到新环境、任务和形态的稳健机器人策略，只需少量额外的机器人特定数据。实验结果表明，DexWild显著提高了性能，在未见环境中实现了68.5%的成功率，几乎是仅使用机器人数据训练的策略的四倍，并提供了5.8倍更好的跨形态泛化能力。视频结果、代码库和说明可在https://dexwild.github.io上找到。
英文摘要：
Large-scale, diverse robot datasets have emerged as a promising path toward enabling dexterous manipulation policies to generalize to novel environments, but acquiring such datasets presents many challenges. While teleoperation provides high-fidelity datasets, its high cost limits its scalability. Instead, what if people could use their own hands, just as they do in everyday life, to collect data? In DexWild, a diverse team of data collectors uses their hands to collect hours of interactions across a multitude of environments and objects. To record this data, we create DexWild-System, a low-cost, mobile, and easy-to-use device. The DexWild learning framework co-trains on both human and robot demonstrations, leading to improved performance compared to training on each dataset individually. This combination results in robust robot policies capable of generalizing to novel environments, tasks, and embodiments with minimal additional robot-specific data. Experimental results demonstrate that DexWild significantly improves performance, achieving a 68.5% success rate in unseen environments-nearly four times higher than policies trained with robot data only-and offering 5.8x better cross-embodiment generalization. Video results, codebases, and instructions at https://dexwild.github.io
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。
