Collaborative manipulation task often requires negotiation using explicit or implicit communication. An important example is determining where to move when the goal destination is not uniquely specified, and who should lead the motion. This work is motivated by the ability of humans to communicate the desired destination of motion through back-and-forth force exchanges. Inherent to these exchanges is also the ability to dynamically assign a role to each participant, either taking the initiative or deferring to the partner's lead. In this paper, we propose a hierarchical robot control framework that emulates human behavior in communicating a motion destination to a human collaborator and in responding to their actions. At the top level, the controller consists of a set of finite-state machines corresponding to different levels of commitment of the robot to its desired goal configuration. The control architecture is loosely based on the human strategy observed in the human-human experiments, and the key component is a real-time intent recognizer that helps the robot respond to human actions. We describe the details of the control framework, and feature engineering and training process of the intent recognition. The proposed controller was implemented on a UR10e robot (Universal Robots) and evaluated through human studies. The experiments show that the robot correctly recognizes and responds to human input, communicates its intent clearly, and resolves conflict. We report success rates and draw comparisons with human-human experiments to demonstrate the effectiveness of the approach.
Context-aware collaborative pushing of heavy objects using skeleton-based intention prediction
Human-Robot Interfaces and Interaction Lab, Istituto Italiano di Tecnologia（人类-机器人接口与交互实验室，意大利技术研究院）；Instituto Israelita de Ensino e Pesquisa, Hospital Israelita Albert Einstein（以色列教育与研究学院，以色列医院阿尔伯特·爱因斯坦医院）
PAINT: Partner-Agnostic Intent-Aware Cooperative Transport with Legged Robots
Robotic Systems Lab, ETH Zurich, Switzerland（瑞士苏黎世联邦理工学院机器人系统实验室）；Computational Robotics Lab, ETH Zurich, Switzerland（瑞士苏黎世联邦理工学院计算机器人学实验室）；ETH AI Center, ETH Zurich, Switzerland（瑞士苏黎世联邦理工学院ETH人工智能中心）
AssistantX: An LLM-Powered Proactive Assistant in Collaborative Human-Populated Environment
Department of Computer Science and Technology, Tsinghua University（计算机科学与技术系，清华大学）；School of Artificial Intelligence, Beijing University of Posts and Telecommunications（人工智能学院，北京邮电大学）
Exploring Human-Robot Collaboration: Analysis of Interaction Modalities in Challenging Tasks
Dalle Molle Institute for Artificial Intelligence (IDSIA), USI-SUPSI（达勒莫勒人工智能研究所（IDSIA））；Department of Surgery, Medicine, Dentistry and Morphological Sciences with interest in Transplant, Oncology and Regenerative Medicine, University of Modena and Reggio Emilia（外科、医学、牙科和形态学科学系（关注移植、肿瘤学和再生医学），摩德纳和雷吉奥艾米利亚大学）；Department of Sciences and Methods for Engineering (DISMI), University of Modena and Reggio Emilia（工程科学与方法系（DISMI），摩德纳和雷吉奥艾米利亚大学）
