Supervised Learning and Reinforcement Learning of Feedback Models for Reactive Behaviors: Tactile Feedback Testbed
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；University of Southern California（南加利福尼亚大学）；Intrinsic Innovation LLC.（Intrinsic创新有限责任公司）；ETH Zurich（苏黎世联邦理工学院）；Google Brain（谷歌大脑）；Dexterity Inc.（Dexterity公司）；Meta Artificial Intelligence（Meta人工智能）
Giovanni Sutanto, Katharina Rombach, Yevgen Chebotar, Zhe Su, Stefan Schaal, Gaurav S. Sukhatme, Franziska Meier
备注：
Accepted for publication in the International Journal of Robotics Research (IJRR). Paper length is 22 pages (including references) with 12 figures. A video overview of the reinforcement learning experiment on the real robot can be seen at https://www.youtube.com/watch?v=yu5v-ZXo4-E. arXiv admin note: text overlap with arXiv:1710.08555
Robots need to be able to adapt to unexpected changes in the environment such that they can autonomously succeed in their tasks. However, hand-designing feedback models for adaptation is tedious, if at all possible, making data-driven methods a promising alternative. In this paper we introduce a full framework for learning feedback models for reactive motion planning. Our pipeline starts by segmenting demonstrations of a complete task into motion primitives via a semi-automated segmentation algorithm. Then, given additional demonstrations of successful adaptation behaviors, we learn initial feedback models through learning from demonstrations. In the final phase, a sample-efficient reinforcement learning algorithm fine-tunes these feedback models for novel task settings through few real system interactions. We evaluate our approach on a real anthropomorphic robot in learning a tactile feedback task.
Tube Diffusion Policy: Reactive Visual-Tactile Policy Learning for Contact-rich Manipulation
Meta Reality Labs Research（Meta现实实验室）；Idiap Research Institute（Idiap研究机构）；École Polytechnique Fédérale de Lausanne（瑞士联邦理工学院）；University of Toronto（多伦多大学）；Purdue University（普渡大学）
Master Micro Residual Correction with Adaptive Tactile Fusion and Force-Mixed Control for Contact-Rich Manipulation
Shenzhen Ubiquitous Data Enabling Key Lab, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China（深圳智能数据赋能重点实验室，深圳国际研究生院，清华大学，深圳518055，中国）；School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China（电子与通信工程学院，中山大学，深圳518107，中国）；School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639956, Singapore（机械与航空航天工程学院，南洋理工大学，新加坡639956，新加坡）
Learning Fine Pinch-Grasp Skills using Tactile Sensing from A Few Real-world Demonstrations
School of Informatics, University of Edinburgh（爱丁堡大学信息学院）；Touchlab Limited（Touchlab有限公司）；Department of Engineering Mathematics, University of Bristol（布里斯托尔大学工程数学系）；Department of Computer Science, University College London（伦敦大学学院计算机科学系）
Learning Physical Interaction: A Survey of Tactile- and Force-aware Robot Learning
Nanyang Technological University（南洋理工大学）；Stanford University（斯坦福大学）；University of California, Berkeley（加利福尼亚大学伯克利分校）；Massachusetts Institute of Technology（麻省理工学院）；National University of Singapore（新加坡国立大学）；Georgia Institute of Technology（佐治亚理工学院）；The University of Tokyo（东京大学）；ETH Zurich（苏黎世联邦理工学院）；Harvard University（哈佛大学）；Imperial College London（伦敦帝国学院）；KTH Royal Institute of Technology（瑞典皇家理工学院）；Technical University of Darmstadt（达姆施塔特工业大学）
Tactile-based force estimation for interaction control with robot fingers
Institut des sytèmes intelligents et de la robotique, Sorbonne University（索邦大学智能与机器人系统研究所）；LIRMM, Montpellier University（蒙彼利埃大学LIRMM实验室）；Nantes Université, École Centrale Nantes, CNRS, LS2N（南特大学、南特中央理工学院、法国国家科学研究中心、南特数字科学实验室）
Towards Tangible Immersion for Cobot Programming-by-Demonstration: Visual, Tactile and Haptic Interfaces for Mixed-Reality Cobot Automation in Semiconductor Manufacturing
Intel Labs, Intelligent System Research, Human-Robot Collaboration（英特尔实验室、智能系统研究、人机协作）
A Tactile-based Interactive Motion Planner for Robots in Unknown Cluttered Environments
Shanghai Research Institute for Intelligent Autonomous Systems（上海智能自主系统研究院）；State Key Laboratory of Autonomous Intelligent Unmanned Systems（自主智能无人系统国家重点实验室）；Frontiers Science Center for Intelligent Autonomous Systems（智能自主系统前沿科学中心）；College of Electronics and Information Engineering, Tongji University（同济大学电子与信息学院）
