发表机构
Nanyang Technological University; Stanford University; University of California, Berkeley; Massachusetts Institute of Technology; National University of Singapore; Georgia Institute of Technology; The University of Tokyo; ETH Zurich; Harvard University; Imperial College London; KTH Royal Institute of Technology; Technical University of Darmstadt（南洋理工大学; 斯坦福大学; 加利福尼亚大学伯克利分校; 麻省理工学院; 新加坡国立大学; 佐治亚理工学院; 东京大学; 苏黎世联邦理工学院; 哈佛大学; 伦敦帝国学院; 瑞典皇家理工学院; 达姆施塔特工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本综述针对力与触觉感知机器人学习研究的空白，提出TF-ART分类法，整合多模态感知与多阶段系统设计视角，兼具算法与实践意义。
AI 中文摘要
基于物理的机器人智能要求机器人感知、推理并调节其与物理世界的交互，该能力在接触敏感型操作中尤为关键，成功完成任务不仅依赖视觉感知与运动生成，还需力调节与自适应控制。在此背景下，近期机器人学习方法通过将力、触觉、视觉、语言及本体感觉感知整合到学习型操作策略中取得了显著进展；同时，许多系统采用多阶段架构，结合高层策略、动作细化模块与低层控制器，以弥合语义任务理解与反应式物理执行之间的差距。尽管取得了这些进展，现有综述尚未从统一视角明确回顾力与触觉感知机器人学习，该视角需同时涵盖多模态感知与多阶段系统设计。本综述通过提出TF-ART（触觉/力感知机器人学习分类法）填补了这一空白，该分类法针对多模态与多阶段框架，将各方法映射到统一的分层结构中。该框架刻画了近期研究如何组织观测模态、编码并融合异质感官输入、跨多阶段生成与细化动作，以及将学习型策略连接到反应式机器人末端控制。基于这一方法论视角，我们进一步考察了物理交互的任务设置与基础设施需求，从而整合了力与触觉感知机器人学习的算法与实践视角。
英文摘要
Physically grounded robot intelligence requires robots to perceive, reason about, and regulate their interactions with the physical world. This capability is particularly critical in contact-sensitive manipulation, where successful task execution depends not only on visual perception and motion generation, but also on force regulation and adaptive control. In this context, recent robot learning methods have made substantial progress by integrating force, tactile, vision, language, and proprioceptive sensing into learned manipulation policies. In parallel, many systems adopt multi-phase architectures that combine high-level policies, action-refinement modules, and low-level controllers to bridge semantic task understanding with reactive physical execution. Despite these advances, existing surveys have not explicitly reviewed force- and tactile-aware robot learning from a unified perspective that jointly captures multimodal sensing and multi-phase system design. This survey addresses this gap by proposing TF-ART, a Tactile/Force-Aware Robot learning Taxonomy for multimodal and multi-phase frameworks, which maps individual methods into a unified hierarchical structure. The framework characterizes how recent works organize observation modalities, encode and fuse heterogeneous sensory inputs, generate and refine actions across multiple phases, and connect learned policies to reactive robot-end control. Building on this methodological view, we further examine the task settings and infrastructure requirements of physical interaction, thereby integrating both algorithmic and practical perspectives on force- and tactile-aware robot learning.
Comments53 pages, 7 figures
