The conventional joystick, the most common input device for powered wheelchairs, occupies one hand during operation and interferes with activities of daily living. Existing hands-free trunk-motion interfaces require extensive trunk movements, making them unsuitable for users with limited stability. Moreover, they increase the risk of falling and psychological load, and calibration schemes that explicitly account for left–right asymmetry in trunk kinematics are lacking. This study introduces a hands-free belt-type interface that senses subtle belt tensions via a six-axis force/torque sensor mounted on the backrest and maps them to translational and angular velocity commands. The interface frees the user’s hands while minimizing the need for large postural adjustments during wheelchair operation. To handle asymmetrical motor function frequently observed in users with physical disabilities, we also introduced a body-axis calibration method.An empirical study involving path-following tasks was conducted with twelve able-bodied participants and two participants with physical disabilities. Statistical non-inferiority testing confirmed that, in the able-bodied group, path-tracking accuracy with the belt interface was not inferior to that with joystick control. In the group with physical disabilities, body-axis calibration significantly enhanced the belt interface’s path-tracking accuracy, narrowing the performance gap to joystick control.

This paper describes a domestic service robot (DSR) that fetches everyday objects and carries them to specified destinations according to free-form natural language instructions. Given an instruction such as “Move the bottle on the left side of the plate to the empty chair,” the DSR is expected to identify the bottle and the chair from multiple candidates in the environment and carry the target object to the destination. Most of the existing multimodal language understanding methods are impractical in terms of computational complexity because they require inferences for all combinations of target object candidates and destination candidates. We propose Switching Head-Tail Funnel UNITER, which solves the task by predicting the target object and the destination individually using a single model. Our method is validated on a dataset based on a standard dataset for Vision-and-Language Navigation with object manipulation tasks. The results show that our method outperforms the baseline method in terms of language comprehension accuracy. Furthermore, we conduct physical experiments in which a DSR delivers standardized everyday objects in a standardized domestic environment as requested by instructions with referring expressions. The experimental results show that the object grasping and placing actions are achieved with success rates of more than 90 %.

Autonomous service robots are increasingly necessary to move without impeding the movement of pedestrians. Previous studies have determined optimal input for robots by minimizing a multi-objective function that includes the cost of reaching the destination and avoiding surrounding pedestrians. However, it is challenging to adjust the weights of each term in the cost function since they depend on the users and environment. In this study, we used the Social Force Model (SFM) as the base cost function and proposed a method to estimate SFM weights preferred by general user based on population density from human feedback. To achieve this, first we use Bayesian optimization and derive each user’s evaluation map of SFM in a virtual reality environment that provides a realistic and immersive experience for subjects to provide feedback on the robot’s movement. Second, we aggregated each user’s evaluation map to estimate a general user’s evaluation map. Finally, we have derived a functional relationship between the preferred SFM weights of general users and population density by Gaussian process regression. This relationship empowers the robot to navigate in a manner preferred by the general public, contingent on population density, even in the absence of human feedback obtained through virtual reality experimentation.

Collaborative delivery robots in hospitals are required to move safely and efficiently in a short time, without colliding with people. Hence, they must consider the risk of people rushing out from blind spots or rooms, including field context such as the role and usage of the location. However, these factors are difficult to extract solely from geometric information. Therefore, we propose a method for generating a rush-out risk map considering the field context from the hospital staf’s operation data of an electric wheelchair. We convert the wheelchair’s speed operated by staff into rush-out risk, and then place rush-out risk potentials at positions where rush-outs may occur. Subsequently, we optimize the mapping position of rush-out risk and parameters of each potential to minimize the error to obtain a rush-out risk map. We collected actual staff operation data in the hospital and confirmed that we could generate a rush-out risk map with small errors.

For the proper functioning of mobile manipulator-type autonomous robot performing complicated tasks in a human-robot coexistence environment, tasks and motions must be planned simultaneously. In such environments, a human and robot should collaborate with each other. Therefore, the robot must act in accordance with the human and avoid useless actions duplicated with those of humans. However, any action undertaken by a human has uncertainty, and thus, predicting them correctly is challenging. This study proposed probabilistic task and motion planning considering both deterministic and probabilistic environment changes caused by robot and human actions temporarily and spatially, respectively. First, the environmental changes were modeled, where the robot is capable of recognizing the possibility of environmental changes. Second, in task planning, the probabilities of each environmental change owing to human actions was minimized. Finally, in motion planning, a movement path connecting each task in a planned order was planned, thereby enabling the robot to perform actions not duplicated with those by a human. Furthermore, the plans generated were compared without considering possibility of human actions and the effectiveness of the proposed method was verified. Consequently, the proposed method was confirmed to reduce the time required for finishing the tasks.

Social robots are used to perform mobile manipulation tasks, such as tidying up and carrying, based on instructions provided by humans. A mobile manipulation planner, which is used to exploit the robot’s functions, requires a better understanding of the feasible actions in real space based on the robot’s subsystem configuration and the object placement in the environment. This study aims to realize a mobile manipulation planner considering the world state, which consists of the robot state (subsystem configuration and their state) required to exploit the robot’s functions. In this paper, this study proposes a novel environmental representation called a world state-dependent action graph (WDAG). The WDAG represents the spatial and temporal order of feasible actions based on the world state by adopting the knowledge representation with scene graphs and a recursive multilayered graph structure. The study also proposes a mobile manipulation planning method using the WDAG. The planner enables the derivation of many effective action sequences to accomplish the given tasks based on an exhaustive understanding of the spatial and temporal connections of actions. The effectiveness of the proposed method is evaluated through practical machine experiments performed. The experimental result demonstrates that the proposed method facilitates the effective utilization of the robot’s functions.

In a human-robot interaction, a robot needs to move to a position where the robot can obtain high reliability data of people, such as positions, postures, and voice. This is because the human recognition reliability depends on the positional relation between the people and the robot. In addition, the robot should choose the sensor data which is necessary to perform the interaction task. Therefore, it is necessary to navigate the robot to the position to obtain the data for initiation of the interaction task. Accordingly, we need to design a path-planning method considering sensor characteristics, human recognition reliability, and task contents. Although previous studies proposed path-planning methods using an interaction potential considering sensor characteristics, they did not consider the task contents and the human recognition reliability, which are important for practical application and did not applied to interaction with multiple people. Consequently, we present a path-planning method considering the task contents and the human recognition reliability using multimodal potential field integrating these information. We verified effectiveness of the path-planning method for interaction with multiple people.