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Our proposed ranking model ranks conversation partners based on self-reported rapport levels for each participant. The model is important for tasks that recommend interaction partners based on user rapport built in past interactions, such as matchmaking between a student and a teacher in one-to-one online language classes. To rank conversation partners, we can use a regression model that predicts rapport ratings. It is, however, challenging to learn the mapping from the participants’ behavior to their associated rapport ratings because a subjective scale for rapport ratings may vary across different participants. Hence, we propose a ranking model trained via preference learning (PL). The model avoids the subjective scale bias because the model is trained to predict ordinal relations between two conversation partners based on rapport ratings reported by the same participant. The input of the model is multimodal (acoustic and linguistic) features extracted from two participants’ behaviors in an interaction. Since there is no publicly available dataset for validating the ranking model, we created a new dataset composed of online dyadic (person-to-person) interactions between a participant and several different conversation partners. We compare the ranking model trained via preference learning with the regression model by using evaluation metrics for the ranking. The experimental results show that preference learning is a more suitable approach for ranking conversation partners. Furthermore, we investigate the effect of each modality and the different stages of rapport development on the ranking performance.

To automate operations in a logistic warehouse, a robot needs to extract items from the clutter on a shelf without collapsing the clutter. To address this problem, this study proposes a multi-step motion planner to stably extract an item by using the support relations of each object included in the clutter. This study primarily focuses on safe extraction, which allows the robot to choose the best next action based on limited observations. By estimating the support relations, we construct a collapse prediction graph to obtain the appropriate order of object extraction. Thus, the target object can be extracted without collapsing the pile. Furthermore, we show that the efficiency of the robot is improved if it uses one of its arms to extract the target object while the other supports a neighboring object. The proposed method is evaluated in real-world experiments on detecting support relations and object extraction tasks. This study makes a significant contribution because the experimental results indicate that the robot can estimate support relations based on collapse predictions and perform safe extraction in real environments. Our multi-step extraction plan ensures both better performance and robustness to achieve safe object extraction tasks from the clutter.

This paper proposes a sliding-mode set-point position controller for hydraulic excavators. The controller employs a simple switching surface that exponentially drives the actuator position to the desired position with a specified time constant. The discrete-time algorithm of the controller is constructed through a double-implicit implementation scheme, which is for implementing a simple sliding-mode controller to nonsmooth actuators, including hydraulic actuators. We employ a nonsmooth quasistatic model of the hydraulic actuator, which analytically accounts for the pressure saturation caused by the relief valves and check valves and the square-root law of the pressure-flowrate relation. This paper elaborates the analytical form of the model to be combined with the double-implicit implementation. A state predictor based on the actuator model extends the controller to compensate for the deadtime in the hydraulic system. The proposed controller is validated with some simulations and also through experiments employing the swing axis of a 13-ton class excavator.