Abstract
This article reviews contemporary methods for integrating force, including both proprioception and tactile sensing, in robot manipulation policy learning. We conduct a comparative analysis on various approaches for sensing force, data collection, behavior cloning, tactile representation learning, and low-level robot control. From our analysis, we articulate when and why forces are needed, and highlight opportunities to improve learning of contact-rich, generalist robot policies on the path toward highly capable touch-based robot foundation models. We generally find that while there are few tasks such as pouring, peg-in-hole insertion, and handling delicate objects, the performance of imitation learning models is not at a level of dynamics where force truly matters. Also, force and touch are abstract quantities that can be inferred through a wide range of modalities and are often measured and controlled implicitly. We hope that juxtaposing the different approaches currently in use will help the reader to gain a systemic understanding and help inspire the next generation of robot foundation models.
Abstract (translated)
本文回顾了将力（包括本体感觉和触觉感知）集成到机器人操作策略学习中的当代方法。我们对各种感测力的方法、数据收集、行为克隆、触觉表示学习以及低级机器人控制进行了比较分析。通过我们的分析，我们阐明了何时以及为何需要使用力，并强调在迈向高度能力的基于触摸的机器人基础模型过程中，改进接触密集型通用机器人策略的学习机会。总体而言，我们发现虽然诸如倾倒液体、插入孔洞、处理易碎物品等任务中确实存在一些应用场景，但模仿学习模型的表现尚未达到动态水平，使得力真正变得重要。此外，力和触觉是可以通过各种模态推断的抽象量，并且通常通过隐式测量和控制。 我们希望对比当前使用的方法能够帮助读者获得系统性理解，并激发下一代机器人基础模型的发展。
URL
https://arxiv.org/abs/2504.11827
