Object insertion is a classic contact-rich manipulation task. The task remains challenging, especially when considering general objects of unknown geometry, which significantly limits the ability to understand the contact configuration between the object and the environment. We study the problem of aligning the object and environment with a tactile-based feedback insertion policy. The insertion process is modeled as an episodic policy that iterates between insertion attempts followed by pose corrections. We explore different mechanisms to learn such a policy based on Reinforcement Learning. The key contribution of this paper is to demonstrate that it is possible to learn a tactile insertion policy that generalizes across different object geometries, and an ablation study of the key design choices for the learning agent: 1) the type of learning scheme: supervised vs. reinforcement learning; 2) the type of learning schedule: unguided vs. curriculum learning; 3) the type of sensing modality: force/torque (F/T) vs. tactile; and 4) the type of tactile representation: tactile RGB vs. tactile flow. We show that the optimal configuration of the learning agent (RL + curriculum + tactile flow) exposed to 4 training objects yields an insertion policy that inserts 4 novel objects with over 85.0% success rate and within 3~4 attempts. Comparisons between F/T and tactile sensing, shows that while an F/T-based policy learns more efficiently, a tactile-based policy provides better generalization.
From Reach to Insert: Tactile-Augmented Precision Assembly under Sub-Millimeter Tolerances
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Extremum Seeking Controlled Wiggling for Tactile Insertion
Department of Electrical and Computer Engineering, University of Maryland, College Park（电气与计算机工程系，马里兰大学学院公园分校）；Department of Computer Science, University of Maryland, College Park（计算机科学系，马里兰大学学院公园分校）；Istituto Italiano di Tecnologia, Via San Quirico, 19 D, Genova, Italy（意大利理工学院，圣奎里科路19D号，热那亚，意大利）；University of Maryland Institute for Advanced Computer Studies, University of Maryland, College Park（马里兰大学高级计算机研究 institute）
VTLA: Vision-Tactile-Language-Action Model with Preference Learning for Insertion Manipulation
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；Samsung R&D Institute China–Beijing（三星中国北京研发中心）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）
Visuo-Tactile Exploration of Unknown Rigid 3D Curvatures by Vision-Augmented Unified Force-Impedance Control
Chair of Robotics and Systems Intelligence, MIRMI-Munich Institute of Robotics and Machine Intelligence, Technical University of Munich（慕尼黑工业大学机器人与系统智能教席，慕尼黑机器人与机器智能研究所）；Centre for Tactile Internet with Human-in-the-Loop (CeTI)（以人为中心的触觉互联网中心）
TacDiffusion: Force-domain Diffusion Policy for Precise Tactile Manipulation
Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich（慕尼黑工业大学慕尼黑机器人与机器智能研究所（MIRMI））；Centre for Tactile Internet with Human-in-the-Loop (CeTI)（以人为中心的触觉互联网中心（CeTI））；Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Active Visuo-Tactile Point Cloud Registration for Accurate Pose Estimation of Objects in an Unknown Workspace
BMW Group（宝马集团）；University of Glasgow（格拉斯哥大学）；Technische Universität München（慕尼黑工业大学）；Donders Institute for Brain and Cognition（唐德斯脑与认知研究所）；Radboud University（拉德堡德大学）
TranTac: Leveraging Transient Tactile Signals for Contact-Rich Robotic Manipulation
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Shenzhen, China（计算机科学与技术学院，哈尔滨工业大学深圳校区）；State Key Laboratory of Smart Farm Technologies and Systems, Harbin, China（智能农业技术与系统国家重点实验室，哈尔滨，中国）
UniTacVLA: Unified Tactile Understanding and Prediction in Vision Language Action Models
Harbin Institute of Technology（哈尔滨工业大学）；Great Bay University（大湾区大学）；Shanghai Jiao Tong University（上海交通大学）；Fudan University（复旦大学）；Nanjing University（南京大学）；Daimon Robotics
HiTac-WAM: A Hierarchical Tactile World Action Model for Contact-Rich Robot Manipulation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；ImprintX Robotics（ImprintX机器人公司）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）
In-Hand Object Pose Estimation via Visual-Tactile Fusion
Department of Computer Science, Goethe Universität Frankfurt, Germany（弗赖堡大学计算机科学系）；Intelligent Autonomous Systems Lab, Department of Computer Science, TU Darmstadt, Germany（图恩-达姆斯塔德大学计算机科学系智能自主系统实验室）；German Research Center for AI (DFKI)（德国人工智能研究中心）；Centre for Cognitive Science, TU Darmstadt（图恩-达姆斯塔德大学认知科学中心）；Hessian Center for Artificial Intelligence (Hessian.AI), Darmstadt（黑森人工智能中心（Hessian.AI），达姆斯塔特）
