To engender safe and efficient human-robot collaboration, it is critical to generate high-fidelity predictions of human behavior. The challenges in making accurate predictions lie in the stochasticity and heterogeneity in human behaviors. This paper introduces a method for human trajectory and intention prediction through a multi-task model that is adaptable across different human subjects. We develop a nonlinear recursive least square parameter adaptation algorithm (NRLS-PAA) to achieve online adaptation. The effectiveness and flexibility of the proposed method has been validated in experiments. In particular, online adaptation can reduce the trajectory prediction error by more than 28% for a new human subject. The proposed human prediction method has high flexibility, data efficiency, and generalizability, which can support fast integration of HRC systems for user-specified tasks.
As assistive and collaborative robots become more ubiquitous in the real-world, we need to develop interfaces and controllers that are safe for users to build trust and encourage adoption. In this Blue Sky paper, we discuss the need for co-evolving task and user-specific safety controllers that can accommodate people's safety preferences. We argue that while most adaptive controllers focus on behavioral adaptation, safety adaptation is also a major consideration for building trust in collaborative systems. Furthermore, we highlight the need for adaptation over time, to account for user's chang…
Human teams exhibit both implicit and explicit intention sharing. To further development of human-robot collaboration, intention recognition is crucial on both sides. Present approaches rely on a vast sensor suite on and around the robot to achieve intention recognition. This relegates intuitive human-robot collaboration purely to such bulky systems, which are inadequate for large-scale, real-world scenarios due to their complexity and cost. In this paper we propose an intention recognition system that is based purely on a portable head-mounted display. In addition robot intention visualisatio…
Humans engaged in collaborative activities are naturally able to convey their intentions to teammates through multi-modal communication, which is made up of explicit and implicit cues. Similarly, a more natural form of human-robot collaboration may be achieved by enabling robots to convey their intentions to human teammates via multiple communication channels. In this paper, we postulate that a better communication may take place should collaborative robots be able to anticipate their movements to human teammates in an intuitive way. In order to support such a claim, we propose a robot system'…
Roma Tre University（罗马第三大学）；University of Cassino and Southern Lazio（卡西诺和南拉齐奥大学）
This paper considers a scenario where a robot and a human operator share the same workspace, and the robot is able to both carry out autonomous tasks and physically interact with the human in order to achieve common goals. In this context, both intentional and accidental contacts between human and robot might occur due to the complexity of tasks and environment, to the uncertainty of human behavior, and to the typical lack of awareness of each other actions. Here, a two stage strategy based on Recurrent Neural Networks (RNNs) is designed to detect intentional and accidental contacts: the occur…
IDAGC: Adaptive Generalized Human-Robot Collaboration via Human Intent Estimation and Multimodal Policy Learning
Chinese Academy of Sciences（中国科学院）；Institute of Automation（自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Zhejiang University（浙江大学）；Beijing Zhongke Huiling Robot Technology Co., LTD（北京中科创联机器人科技有限公司）
DTRT: Enhancing Human Intent Estimation and Role Allocation for Physical Human-Robot Collaboration
CAS Engineering Laboratory for Intelligent Industrial Vision, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所CAS工程视觉实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Carnegie Mellon University（卡内基梅隆大学）
A robot needs multiple interaction modes to robustly collaborate with a human in complicated industrial tasks. We develop a Coexistence-and-Cooperation (CoCo) human-robot collaboration system. Coexistence mode enables the robot to work with the human on different sub-tasks independently in a shared space. Cooperation mode enables the robot to follow human guidance and recover failures. A human intention tracking algorithm takes in both human and robot motion measurements as input and provides a switch on the interaction modes. We demonstrate the effectiveness of CoCo system in a use case analo…
Istituto Italiano di Tecnologia（意大利技术研究院）；Politecnico di Milano（米兰理工大学）
The recognition of actions performed by humans and the anticipation of their intentions are important enablers to yield sociable and successful collaboration in human-robot teams. Meanwhile, robots should have the capacity to deal with multiple objectives and constraints, arising from the collaborative task or the human. In this regard, we propose vision techniques to perform human action recognition and image classification, which are integrated into an Augmented Hierarchical Quadratic Programming (AHQP) scheme to hierarchically optimize the robot's reactive behavior and human ergonomics. The…
Carnegie Mellon University（卡内基梅隆大学）；University of Washington（华盛顿大学）
We present the Human And Robot Multimodal Observations of Natural Interactive Collaboration (HARMONIC) data set. This is a large multimodal data set of human interactions with a robotic arm in a shared autonomy setting designed to imitate assistive eating. The data set provides human, robot, and environmental data views of twenty-four different people engaged in an assistive eating task with a 6 degree-of-freedom (DOF) robot arm. From each participant, we recorded video of both eyes, egocentric video from a head-mounted camera, joystick commands, electromyography from the forearm used to opera…
Stochastic Decision-Making Framework for Human-Robot Collaboration in Industrial Applications
Department of Control and Instrumentation Engineering, King Fahd University of Petroleum and Minerals (KFUPM)（控制与仪器工程系，国王法赫德石油与矿物大学）；Interdisciplinary Research Center for Intelligent Manufacturing & Robotics, King Fahd University of Petroleum and Minerals (KFUPM)（智能制造与机器人交叉研究中心，国王法赫德石油与矿物大学）
Institut de Robòtica i Informàtica Industrial (CSIC-UPC)（工业机器人与信息研究所（西班牙国家研究委员会-加泰罗尼亚理工大学））
In this work we argue that in Human-Robot Collaboration (HRC) tasks, the Perception-Action cycle in HRC tasks can not fully explain the collaborative behaviour of the human and robot and it has to be extended to Perception-Intention-Action cycle, where Intention is a key topic. In some cases, agent Intention can be perceived or inferred by the other agent, but in others, it has to be explicitly informed to the other agent to succeed the goal of the HRC task. The Perception-Intention-Action cycle includes three basic functional procedures: Perception-Intention, Situation Awareness and Action. T…
Okinawa Institute of Science and Technology（冲绳科学技术大学院大学）
Embodiment and subjective experience in human-robot interaction are important aspects to consider when studying both natural cognition and adaptive robotics to human environments. Although several researches have focused on nonverbal communication and collaboration, the study of autonomous physical interaction has obtained less attention. From the perspective of neurorobotics, we investigate the relation between intentionality, motor compliance, cognitive compliance, and behavior emergence. We propose a variational model inspired by the principles of predictive coding and active inference to s…
Equipping robots with the ability to infer human intent is a vital precondition for effective collaboration. Most computational approaches towards this objective derive a probability distribution of "intent" conditioned on the robot's perceived state. However, these approaches typically assume task-specific labels of human intent are known a priori. To overcome this constraint, we propose the Disentangled Sequence Clustering Variational Autoencoder (DiSCVAE), a clustering framework capable of learning such a distribution of intent in an unsupervised manner. The proposed framework leverages rec…
Common experience suggests that agents who know each other well are better able to work together. In this work, we address the problem of calibrating intention and capabilities in human-robot collaboration. In particular, we focus on scenarios where the robot is attempting to assist a human who is unable to directly communicate her intent. Moreover, both agents may have differing capabilities that are unknown to one another. We adopt a decision-theoretic approach and propose the TICC-POMDP for modeling this setting, with an associated online solver. Experiments show our approach leads to bette…
A Distributed Multi-Modal Sensing Approach for Human Activity Recognition in Real-Time Human-Robot Collaboration
University of Genoa（热那亚大学）；The University of Danang–University of Science and Technology（丹江-科学技术大学）；Japan Advanced Institute of Science and Technology（日本先进科学研究院）
SUNY Binghamton（纽约州立大学宾汉姆顿分校）；OPPO US Research Center（OPPO美国研究中心）
Human-robot collaboration frequently requires extensive communication, e.g., using natural language and gestures. Augmented reality (AR) has provided an alternative way of bridging the communication gap between robots and people. However, most current AR-based human-robot communication methods are unidirectional, focusing on how the human adapts to robot behaviors, and are limited to single-robot domains. In this paper, we develop AR for Robots Collaborating with a Human (ARROCH), a novel algorithm and system that supports bidirectional, multi-turn, human-multi-robot communication in indoor mu…
When autonomous vehicles still struggle to solve challenging situations during on-road driving, humans have long mastered the essence of driving with efficient transferable and adaptable driving capability. By mimicking humans' cognition model and semantic understanding during driving, we present HATN, a hierarchical framework to generate high-quality driving behaviors in multi-agent dense-traffic environments. Our method hierarchically consists of a high-level intention identification and low-level action generation policy. With the semantic sub-task definition and generic state representatio…
