Turn-taking is essential to the structure of human teamwork. Humans are typically aware of team members' intention to keep or relinquish their turn before a turn switch, where the responsibility of working on a shared task is shifted. Future co-robots are also expected to provide such competence. To that end, this paper proposes the Cognitive Turn-taking Model (CTTM), which leverages cognitive models (i.e., Spiking Neural Network) to achieve early turn-taking prediction. The CTTM framework can process multimodal human communication cues (both implicit and explicit) and predict human turn-taking intentions in an early stage. The proposed framework is tested on a simulated surgical procedure, where a robotic scrub nurse predicts the surgeon's turn-taking intention. It was found that the proposed CTTM framework outperforms the state-of-the-art turn-taking prediction algorithms by a large margin. It also outperforms humans when presented with partial observations of communication cues (i.e., less than 40 of full actions). This early prediction capability enables robots to initiate turn-taking actions at an early stage, which facilitates collaboration and increases overall efficiency.
Physical turn-taking refers to the process where a hybrid human-robot team take turns on a physical task. Turn-taking has been studied in robotics and HRI to regulate shared resources among team members. Those resources include time (i.e., only one person can work on the shared task at a time) and space (i.e., only one person can access the working space at a time). In the context of human robot interaction, the types and usage frequencies of various implicit communication cues have been studied in a robot-assisted assembly task @cite_13 . The timing in multimodal turn-taking (i.e., speech, gaze, and gesture) was investigated through a collaborative Towers of Hanoi challenge accomplished by a hybrid human robot team @cite_1 . Nonverbal cues for timing coordination in physical turn-taking were studied in manufacturing @cite_9 . Such research focuses on turn-taking process modelling and the robot control, while neglecting predictions of the turn-taking timing and intention.
@cite_1 @cite_9 @cite_13
2011685492 2553961644 2157719307 2418222507
Nonverbal communication cues play an important role in human-human interaction and are expected to take a similar role in human-robot collaboration. In current industrial practice, human-robot turn-taking is explicitly human controlled, via a command channel such as switch or button. However, such a master-slave approach does not permit collaborative interaction, and requires the human to focus on both controlling the robot's behavior and on the task, thereby affecting overall performance. In this paper, implicit, nonverbal communication cues are examined as a non-explicit communication channel during a turn-taking task context. The aim of this study is to characterize the types and frequencies of nonverbal cues important to regulating turn taking during an assembly-task-type collaboration. This analysis will guide the selection of cues that can be expressed by the robot as implicit user inputs while human and robot complete a shared task. Humans naturally use the multiple modalities of speech, gesture, and gaze when they communicate. They also engage in turn-taking to manage the seizing and yielding of the speaking oor, a process that controls the execution of turns comprising discrete speech, gesture, and gaze events. We are interested in modeling such interaction processes for a social robot to use that can be transferred between dierent domains. Timed Petri nets (TPNs) are currently uncommon in human-robot interaction (HRI) but oer an attractive representation for modeling concurrency and synchronization when controlling behavior for multiple modalities. Their representation also permits the intuitive and modular combination of rule-based behavior expression with statistical timing models. We describe their utility in application to multimodal interaction and compare them to nite state This paper presents the application of a statistical framework that allows to endow a humanoid robot with the ability to perform a collaborative manipulation task with a human operator. We investigate to what extent the dynamics of the motion and the haptic communication process that takes place during physical collaborative tasks can be encapsulated by the probabilistic model. This framework encodes the dataset in a Gaussian Mixture Model, which components represent the local correlations across the variables that characterize the task. A set of demonstrations is performed using a bilateral coupling teleoperation setup; then the statistical model is trained in a pure follower leader role distribution mode between the human and robot alternatively. The task is reproduced using Gaussian Mixture Regression. We present the probabilistic model and the experimental results obtained on the humanoid platform HRP-2; preliminary results assess our theory on switching behavior modes in dyad collaborative tasks: when reproduced with users which were not instructed to behave in either a follower or a leader mode, the robot switched automatically between the learned leader and follower behaviors. To enable safe and efficient human–robot collaboration in shared workspaces, it is important for the robot to predict how a human will move when performing a task. While predicting human motion for tasks not known a priori is very challenging, we argue that single-arm reaching motions for known tasks in collaborative settings (which are especially relevant for manufacturing) are indeed predictable. Two hypotheses underlie our approach for predicting such motions: First, that the trajectory the human performs is optimal with respect to an unknown cost function, and second, that human adaptation to their partner's motion can be captured well through iterative replanning with the above cost function. The key to our approach is thus to learn a cost function that “explains” the motion of the human. To do this, we gather example trajectories from pairs of participants performing a collaborative assembly task using motion capture. We then use inverse optimal control to learn a cost function from these trajectories. Finally, we predict reaching motions from the human's current configuration to a task-space goal region by iteratively replanning a trajectory using the learned cost function. Our planning algorithm is based on the trajectory optimizer: stochastic trajectory optimizer for motion planning [1] ; it plans for a 23-degree-of-freedom human kinematic model and accounts for the presence of a moving collaborator and obstacles in the environment. Our results suggest that in most cases, our method outperforms baseline methods when predicting motions. We also show that our method outperforms baselines for predicting human motion when a human and a robot share the workspace.