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.

To facilitate the integration of autonomous mobile robots into environments shared with humans, ensuring the comfort of pedestrians and observers (individuals observing the interaction from a third-party perspective) is crucial. This study explores the correlation between pedestrian and observer comfort and the robot’s distance and pose angle during pedestrian encounters. The findings indicate that when a robot passes a pedestrian after completing its avoidance motion, it enhances the comfort of both pedestrians and observers. In addition, when a robot maintains a large distance from pedestrians while passing by, it improves pedestrian comfort. Furthermore, designing the robot’s pose during passing so that it appears to move away from the pedestrian from the observer’s perspective further increases the observer’s comfort.

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.

For a companion robot that follows a person as an assistant, predicting human walking is important to produce a proactive movement that is helpful to maintain an appropriate area decided by the human personal space. However, fully trusting the prediction may result in obstructing human walking because it is not always accurate. Hence, we consider the estimation of uncertainty (i.e., entropy) of the prediction to enable the robot to move without causing overconfident motion and without being late for the person it follows. To consider this uncertainty of the prediction to the controller, we introduce a reliability value that changes based on the entropy of the prediction. This value expresses the extent the controller should trust the prediction result, and it affects the cost function of our controller. We propose an uncertainty-aware robot controller based on nonlinear model predictive control to realize natural human-followings. We found that our uncertainty-aware control system can produce an appropriate robot movement, such as not obstructing the human walking and avoiding delay, in both simulations using actual human walking data and real-robot experiments.

For the evaluation of pathological gait, a machine learning-based estimation of the vertical ground reaction force (vGRF) using a low-cost insole is proposed as an alternative to costly force plates. However, learning a model for estimation still relies on the use of force plates, which is not accessible in small clinics and individuals. Therefore, this paper presents a force plate-free learning from a single leg stance (SLS) and natural walking measured only by the insoles. This method used a linear least squares regression that fits insole measurements during SLS to body weight in order to learn a model to estimate vGRF during walking. Constraints were added to the regression so that vGRF estimates during walking were of proper magnitude, and the constraint bounds were newly defined as a linear function of stance duration. Moreover, a lower bound for the estimated vGRF in mid-stance was added to the constraints to enhance estimation accuracy. The vGRF estimated by the proposed method was compared with force platforms for 4 healthy young adults and 13 elderly adults including patients with mild osteoarthritis, knee pain, and valgus hallux. Through the experiments, the proposed learning method had a normalized root mean squared error under 10% for healthy young and elderly adults with stance durations within a certain range (600-800 ms). From these results, the validity of the proposed learning method was verified for various users requiring assessment in the field of medicine and healthcare.

Tracking of human legs during walking are key technologies for gait analysis evaluating the movement function of the elderly and patients with gait disorders. Although the motion capture cameras are the gold standard method for gait analysis because of their high accuracy, they are not always accessible in clinical sites because of their cost, scale, and usability. In response, a laser range sensor (LRS), which is used for obstacle avoidance and human detection of mobile robots, has recently been employed for tracking of leg motions. Some previous studies set LRS at shin height and tracked leg motions during walking using three or five observation patterns and the Kalman filtering and data association methods. However, these systems had difficulty in tracking during walking along a circular trajectory including frequent overlaps and occlusions of legs. Therefore, this paper presents a spatiotemporal and kinetic gait analysis system using a single LRS and instrumented insoles and proposes a multisensor fusion algorithm for tracking leg motions. The instrumented insoles are in-shoe devices embedded force sensors and can detect accurate timings of gait events via force sensing. The system identifies gait phases by the fusion algorithm and switches acceleration input added to motion models of tracked legs for the Kalman filter and data association. The tracking performance of the proposed system was evaluated by measuring walking on a circular trajectory in experiments.

The majority of learning from demonstration approaches do not address suboptimal demonstrations or cases when drastic changes in the environment occur after the demonstrations were made. For example, in real teleoperation tasks, the demonstrations provided by the user are often suboptimal due to interface and hardware limitations. In tasks involving co-manipulation and manipulation planning, the environment often changes due to unexpected obstacles rendering previous demonstrations invalid. This paper presents a reinforcement learning algorithm that exploits the use of relevance functions to tackle such problems. This paper introduces the Pearson correlation as a measure of the relevance of policy parameters in regards to each of the components of the cost function to be optimized. The method is demonstrated in a static environment where the quality of the teleoperation is compromised by the visual interface (operating a robot in a three-dimensional task by using a simple 2D monitor). Afterward, we tested the method on a dynamic environment using a real 7-DoF robot arm where distributions are computed online via Gaussian Process regression.

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.

In this paper, dual-task performance assessment robot (DAR) using projection is developed. Falling is a common problem in the growing elderly population. Fall-risk assessment systems have proven to be helpful in community-based fall prevention programs. One of the risk factors of falling is the deterioration of a person's dual-task performance. For example, gait training, which enhances both motor and cognitive functions, is a multi-target stepping task (MTST), in which participants step on assigned colored targets. To evaluate the dual-task performance during MTST in human living space, projection mapping and robot navigation to maintain a safe distance from the participant are key technologies. Projection mapping is used to evaluate the long-distance dual-task performance, where MTST images are displayed on the floor by the moving DAR. To evaluate the accuracy of the projected target position, experiments for MTST projection using the moving DAR and video analysis are carried out. Additionally, to verify the validity of the MTST by the moving DAR at a constant speed, experiments with several young participants are carried out.

Kinetic and spatiotemporal gait analysis provides important information for the treatment and rehabilitation of patients with gait disorders. Although motion analysis laboratories with infrared motion capture cameras and force plates are the gold standard measurement technologies for analysis, they are, in most cases, impractical for community health centers or general clinical settings because of their cost, scale, and usability. This paper presents an accessible gait analysis system that uses simultaneous measurements of instrumented insoles and a laser range sensor (LRS). In the proposed system, GRF and COP are estimated by sensor analysis taken from the insoles. Strides step width, cadence, and walking speed are measured by the LRS. In addition, stance durations including single limb support and double stance are detected at the insoles from accurate measurements of initial and end foot contact time. The GRF estimation method derived from the insole sensors and the integrated system with the LRS have been validated by measuring straight walking and comparing results with data obtained from motion analysis laboratories.

Falling is a common problem in the growing elderly population and fall-risk assessment systems are needed for community-based fall prevention program. In particular, gait measurements such as several-meters walk tests are carried out in community health activities. To evaluate the walking ability of the participant, it is necessary to measure foot contact positions so that the walking parameters such as stride length can be used for fall-risk assessment. However, the conventional measurement systems are difficult to install for use in community health activities because of their scale, cost and constraints of the measurement range. Therefore, we propose a gait measurement robot (GMR) using laser range sensor (LRS) for a long-distance walk tests. From the experimental results with young people, it was confirmed that the GMR could measure the both leg trajectory and the foot contact positions. However, in the case of the elderly especially, a false tracking is likely to occur due to the narrow stride. In addition, the GMR calculates the foot contact positions by analyzing the estimated position and speed of each leg. In the case of the elderly, there is a possibility that the GMR cannot detect the foot contact correctly because the walking speed is likely to be slow. In this study, we carry out the seven-meter straight walk tests with the elderly people using a stationary LRS for the advance verification of measurement in the elderly with the GMR. We verify the leg tracking and foot contact detection using a stationary LRS in the elderly compared with the video analysis.

To prevent falls in the elderly, gait measurements such as several-meters walking test and gait trainings are carried out in community health activities. To evaluate the risk of falling of the participant, it is necessary to measure foot contact times and positions so that the stride length of each leg and the walking speed can be used as evaluation parameters. However, the conventional measurement systems are difficult to install for use in community health activities because of their scale, cost and constraints of the measurement range. In this study, we propose a novel gait measurement system which uses an autonomous mobile robot with laser range sensor (LRS) for a long-distance walking test in a real living space regardless of detection range of sensor. The robot sequentially estimates its own pose and acquires the position of both legs of the participant. The robot leads the participant from the start to the goal of the walking test while maintaining a certain distance from the participant. Then, the foot contact times and the positions are calculated by analyzing estimated position and speed of each leg. From the experimental results, it was confirmed that the proposed robot could acquire the foot contact times and positions.

To date, most passive walkers have been designed with arc-shaped feet rigidly attached to the legs. We developed a simple 3D straight-legged passive walker with flat feet and ankle springs. The flat feet are connected to the legs with springs at the ankles that produce torsional force while the stance leg is on the ground, mimicking the motion of simple 3D passive walkers with arc-shaped feet. This helps to prevent slipping and to handle the disturbance behavior of the robot. Flat feet are particularly advantageous for yaw stability. Our 3D passive-walker robot with a 0.84-m leg can walk the full length of a 1.8-m slope at about 0.44 m/s.

This study presents a design technique of an efficient biped walking robot on level ground with a simple mechanism based on passive-dynamic walking. A torso is used to generate active power replacing gravity used in passive walk. Swing-leg control is introduced to create a steady gait. Numerical simulations show that a biped robot with knees and a torso can walk efficiently on level ground. When we choose an appropriate parameter of the swing-leg control, the biped robot can walk stably over a wide range of speed. Furthermore, the walking performance of the robot increases with the increase of the radius of circular feet.

This study aims at finding active biped robot designs with efficiency and simplicity of passive-dynamic walking. In this paper, it is shown that a biped robot with torso can walk efficiently on level ground over a wide range of speed by using torso and swing leg control based on passive-dynamic walking. A torso is used to generate active power replacing gravity, proposed by McGeer. The biped robot can exhibit a stable gait not planed in advance, and a period-doubling bifurcation is demonstrated in numerical simulations. Furthermore, when we choose carefully a swing leg control gain, a reverse period-doubling bifurcation from chaotic gaits to period-one gaits is demonstrated, which is not found in the passive-dynamic walking.

This study aims at establishing a new control strategy for more natural and efficient bipedal locomotion. In this study, the robot is modeled as a planar biped model composed of a torso, hips, and two different legs with knees, but without ankles. The proposed method consists of central pattern generator (CPG) for legged locomotion and torso attitude control. It is well known that the CPG controller copes with environmental changes by mutual entrainment of the oscillatory activities of the CPG and the body. Therefore, the biped robot can walk on both a level ground and a slope, and has the robustness for environmental changes. Moreover, the torso attitude control is executed concurrently with CPG controller for legged locomotion in the method. By utilizing the interaction between torso and legs, the biped robot with the torso can walk on the level ground over a wide range of speed. This paper presents a systematic control design method of the proposed strategy by using the genetic algorithm. In order to verify the effectiveness of the proposed method, computational simulations were carried out. As a result, it was demonstrated that the biped robot can walk on the level ground at a variable pace according to the desired torso angle given as an external command. Moreover, it was confirmed that the proposed controller has the robustness for environmental changes and external disturbance, and the biped robot can walk naturally on the uphill and downhill slopes.