Improving Human-Computer Cooperation Through Haptic Role Exchange and Negotiation

Küçükyılmaz, Ayşe; Oguz, Ozgur S.; Sezgin, T. Metin; Başdoğan, Çağatay

doi:10.1007/978-1-4471-2754-3_13

Cited by 8 publications

(3 citation statements)

References 14 publications

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“…In the context of pHRI, shared control is a way to take into account human operation characteristics to proactively alter robotic operation. This may involve enabling the human-robot team to dynamically arbitrate leader and follower roles as done in [12], [13], [14], [15], [16], [17], [18], [19]. Collaborative arbitration is defined by dynamic role exchanges between the human and the robot.…”

Section: Introductionmentioning

confidence: 99%

Online Identification of Interaction Behaviors From Haptic Data During Collaborative Object Transfer

Küçükyılmaz

Issak

2020

IEEE Robot. Autom. Lett.

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Joint object transfer is a complex task, which is less structured and less specific than what is existing in several industrial settings. When two humans are involved in such a task, they cooperate through different modalities to understand the interaction states during operation and mutually adapt to one another's actions. Mutual adaptation implies that both partners can identify how well they collaborate (i.e. infer about the interaction state) and act accordingly. These interaction states can define whether the partners work in harmony, face conflicts, or remain passive during interaction. Understanding how two humans work together during physical interactions is important when exploring the ways a robotic assistant should operate under similar settings. This study acts as a first step to implement an automatic classification mechanism during ongoing collaboration to identify the interaction state during object comanipulation. The classification is done on a dataset consisting of data from 40 subjects, who are partnered to form 20 dyads. The dyads experiment in a physical human-human interaction (pHHI) scenario to move an object in an haptics-enabled virtual environment to reach predefined goal configurations. In this study, we propose a sliding-window approach for feature extraction and demonstrate the online classification methodology to identify interaction patterns. We evaluate our approach using 1) a support vector machine classifier (SVMc) and 2) a Gaussian Process classifier (GPc) for multi-class classification, and achieve over 80% accuracy with both classifiers when identifying general interaction types.

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Section: Introductionmentioning

confidence: 99%

Online Identification of Interaction Behaviors From Haptic Data During Collaborative Object Transfer

Küçükyılmaz

Issak

2020

IEEE Robot. Autom. Lett.

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“…This was done to avoid implicit role allocations between partners, and ease the data annotation process in our experiment. However, role definitions are key in human interactions and were demonstrated to be effective mechanisms to improve collaboration in our earlier work [5], [6], [15]. In future work, we plan to explore online interaction behavior classification as a way to investigate how conflicts arise and are resolved if subjects are also allowed to take on leadership roles.…”

Section: Discussionmentioning

confidence: 99%

A Novel Haptic Feature Set for the Classification of Interactive Motor Behaviors in Collaborative Object Transfer

Al-Saadi

Sirintuna

Küçükyılmaz

et al. 2021

IEEE Trans. Haptics

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Haptics provides a natural and intuitive channel of communication during the interaction of two humans in complex physical tasks, such as joint object transportation. However, despite the utmost importance of touch in physical interactions, the use of haptics is under-represented when developing intelligent systems. This study explores the prominence of haptic data to extract information about underlying interaction patterns within physical human-human interaction (pHHI). We work on a joint object transportation scenario involving two human partners, and show that haptic features, based on force/torque information, suffice to identify human interactive behavior patterns. We categorize the interaction into four discrete behavior classes. These classes describe whether the partners work in harmony or face conflicts while jointly transporting an object through translational or rotational movements. In an experimental study, we collect data from 12 human dyads and verify the salience of haptic features by achieving a correct classification rate over 91% using a Random Forest classifier.

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“…Although such robots have transformative capability by delivering beyond human performance through highly precise, rapid and stable movements in far more degrees of freedom than the human hand, fully autonomous solutions are not feasible due to environmental uncertainities and complex decision requirements of the tasks. This brings forward a need for developing shared control systems [5], [6], [7], where both humans and robots may benefit from one another's capabilities by dynamically and appropriately Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

Intent-Aware Predictive Haptic Guidance and its Application to Shared Control Teleoperation

Poozhiyil

Pandya

et al. 2021

2021 30th IEEE International Conference on Robot &Amp; Human Interactive Communication (RO-MAN)

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This paper presents a haptic shared control paradigm that modulates the level of robotic guidance, based on predictions of human motion intentions. The proposed method incorporates robot trajectories learned from human demonstrations and dynamically adjusts the level of robotic assistance based on how closely the detected intentions match these trajectories. An experimental study is conducted to demonstrate the paradigm on a teleoperated pick-and-place task using a Franka Emika Panda robot arm, controlled via a 3D Systems Touch X haptic interface. In the experiment, the human operator teleoperates a remote robot arm while observing the environment on a 2D screen. While the human teleoperates the robot arm, the objects are tracked, and the human's motion intentions (e.g., which object will be picked or which bin will be approached) are predicted using a Deep Q-Network (DQN). The predictions are made considering the current robot state and baseline robot trajectories that are learned from human demonstrations using Probabilistic Movement Primitives (ProMPs). The detected intentions are then used to condition the ProMP trajectories to modulate the movement and accommodate changing object configurations. Consequently, the system generates adaptive force guidance as weighted virtual fixtures that are rendered on the haptic device. The outcomes of the user study, conducted with 12 participants, indicate that the proposed paradigm can successfully guide users to robust grasping configurations and brings better performance by reducing the number of grasp attempts and improving trajectory smoothness and length.

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Improving Human-Computer Cooperation Through Haptic Role Exchange and Negotiation

Cited by 8 publications

References 14 publications

Online Identification of Interaction Behaviors From Haptic Data During Collaborative Object Transfer

Online Identification of Interaction Behaviors From Haptic Data During Collaborative Object Transfer

A Novel Haptic Feature Set for the Classification of Interactive Motor Behaviors in Collaborative Object Transfer

Intent-Aware Predictive Haptic Guidance and its Application to Shared Control Teleoperation

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