This paper develops a methodology for collaborative human-robot exploration that leverages implicit coordination. Most autonomous single- and multi-robot exploration systems require a remote operator to provide explicit guidance to the robotic team. Few works consider how to embed the human partner alongside robots to provide guidance in the field. A remaining challenge for collaborative human-robot exploration is efficient communication of goals from the human to the robot. In this paper we develop a methodology that implicitly communicates a region of interest from a helmet-mounted depth camera on the human's head to the robot and an information gain-based exploration objective that biases motion planning within the viewpoint provided by the human. The result is an aerial system that safely accesses regions of interest that may not be immediately viewable or reachable by the human. The approach is evaluated in simulation and with hardware experiments in a motion capture arena. Videos of the simulation and hardware experiments are available at: https://youtu.be/7jgkBpVFIoE.
AMSwarmX: Safe Swarm Coordination in CompleX Environments via Implicit Non-Convex Decomposition of the Obstacle-Free Space
University of Toronto Institute for Aerospace Studies（多伦多大学航空航天研究所）；Technical University of Munich（慕尼黑工业大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；University of Tartu（塔尔图大学）
Learning Human-Humanoid Coordination for Collaborative Object Carrying
Department of Electrical and Electronic Engineering, the University of Hong Kong（香港大学电子与电气工程系）；State Key Laboratory of General Artificial Intelligence, BIGAI（通用人工智能国家重点实验室，BIGAI）；School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；Yuanpei College, Peking University（北京大学元培学院）
Cognition to Control - Multi-Agent Learning for Human-Humanoid Collaborative Transport
Department of Electrical Engineering, the University of Texas at Arlington（电气工程系，德克萨斯大学阿灵顿分校）；Department of Mechanical Engineering, Carnegie Mellon University（机械工程系，卡内基梅隆大学）
Implicit Behavior Coordination from Sub-Task Demonstrations by Exploiting Overlap-Induced Multimodality
Humanoid Robots Lab and Center for Robotics, University of Bonn（人形机器人实验室及机器人技术中心，波恩大学）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究所）
Can LLM Agents Solve Collaborative Tasks? A Study on Urgency-Aware Planning and Coordination
Computer Vision and Robotics Laboratory (VeRLab), Department of Computer Science, Universidade Federal de Minas Gerais（计算机视觉与机器人实验室（VeRLab）、计算机科学系、巴西联邦大学矿务格里斯大学）
Whole-Body Control for Velocity-Controlled Mobile Collaborative Robots Using Coupling Dynamic Movement Primitives
Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS)（深圳人工智能与机器人研究院（AIRS））；Harbin Institute of Technology（哈尔滨工业大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
ACHORD: Communication-Aware Multi-Robot Coordination with Intermittent Connectivity
Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）；Instituto Tecnológico Vale（淡水河谷技术研究所）；University of Southern California（南加州大学）；Cornell University（康奈尔大学）；University of California, Riverside（加州大学河滨分校）；Polytechnique Montréal（蒙特利尔理工学院）；Institut de Robòtica i Informàtica Industrial, CSIC-UPC（西班牙高等科学研究委员会-加泰罗尼亚理工大学机器人与工业信息研究所）；California Institute of Technology（加州理工学院）
