Caregiving is a vital role for domestic robots, especially the repositioning care has immense societal value, critically improving the health and quality of life of individuals with limited mobility. However, repositioning task is a challenging area of research, as it requires robots to adapt their motions while interacting flexibly with patients. The task involves several key challenges: (1) applying appropriate force to specific target areas; (2) performing multiple actions seamlessly, each requiring different force application policies; and (3) motion adaptation under uncertain positional conditions. To address these, we propose a deep neural network (DNN)-based architecture utilizing proprioceptive and visual attention mechanisms, along with impedance control to regulate the robot's movements. Using the dual-arm humanoid robot Dry-AIREC, the proposed model successfully generated motions to insert the robot's hand between the bed and a mannequin's back without applying excessive force, and it supported the transition from a supine to a lifted-up position. The project page is here: https://sites.google.com/view/caregiving-robot-airec/repositioning

In many contact-rich tasks, force sensing plays an essential role in adapting the motion to the physical properties of the manipulated object. To enable robots to capture the underlying distribution of object properties necessary for generalising learnt manipulation tasks to unseen objects, existing Learning from Demonstration (LfD) approaches require a large number of costly human demonstrations. Our proposed semi-supervised LfD approach decouples the learnt model into a haptic representation encoder and a motion generation decoder. This enables us to pre-train the first using a large amount of unsupervised data, easily accessible, while using few-shot LfD to train the second, leveraging the benefits of learning skills from humans. We validate the approach on the wiping task using sponges with different stiffness and surface friction. Our results demonstrate that pre-training significantly improves the ability of the LfD model to recognise physical properties and generate desired wiping motions for unseen sponges, outperforming the LfD method without pre-training. We validate the motion generated by our semi-supervised LfD model on the physical robot hardware using the KUKA iiwa robot arm. We also validate that the haptic representation encoder, pre-trained in simulation, captures the properties of real objects, explaining its contribution to improving the generalisation of the downstream task. See our accompanying video: https://youtu.be/zP4JvHaCWHk.

Recognising the characteristics of objects while a robot handles them is crucial for adjusting motions that ensure stable and efficient interactions with containers. Ahead of realising stable and efficient robot motions for handling/transferring the containers, this work aims to recognise the unobservable latent object characteristics. While vision is commonly used for object recognition by robots, it is ineffective for detecting hidden objects. However, recognising objects indirectly using other sensors is a challenging task. To address this challenge, we propose a cross-modal transfer learning approach from vision to haptic-audio. We initially train the model with vision, directly observing the target object. Subsequently, we transfer the latent space learned from vision to a second module, trained only with haptic-audio and motor data. This transfer learning framework facilitates the representation of object characteristics using indirect sensor data, thereby improving recognition accuracy. For evaluating the recognition accuracy of our proposed learning framework we selected shape, position, and orientation as the object characteristics. Finally, we demonstrate online recognition of both trained and untrained objects using the humanoid robot Nextage Open. See our accompanying video here: https://www.youtube.com/watch?v=sOHqPC1uusg

For assisting humans in their daily lives, robots need to perform long-horizon tasks, such as tidying up a room or preparing a meal. One effective strategy for handling a long-horizon task is to break it down into short-horizon subgoals, that the robot can execute sequentially. In this paper, we propose extending a predictive learning model using deep neural networks (DNN) with a Subgoal Proposal Module (SPM), with the goal of making such tasks realizable. We evaluate our proposed model in a case-study of a long-horizon task, consisting of cutting and arranging a pizza. This task requires the robot to consider: (1) the order of the subtasks, (2) multiple subtask selection, (3) coordination of dual-arm, and (4) variations within a subtask. The results confirm that the model is able to generalize motion generation to unseen tools and objects arrangement combinations. Furthermore, it significantly reduces the prediction error of the generated motions compared to without the proposed SPM. Finally, we validate the generated motions on the dual-arm robot Nextage Open. See our accompanying video here: https://youtu.be/3hYS2knRm50

These days, value structure and social structure are changing with the background of immigration, globalization, and diversification. In many countries, such as Japan, a foreign workforce is introduced due to ageing citizens and labour shortages. Such diversification would be a great opportunity to create innovation. Innovation is often achieved when ideas from different perspectives synergize with each other. In this paper, we focus on the job hunting scene and suggest recruiting diverse and cooperative teams as one form of recruitment. Conventional job-hunting services and Social networking services (SNSs) are limited to matching labour supply and demand or attracting people with similar values. To help build and recruit diverse and cooperative teams, we propose a system to build a platform to connect people with different backgrounds, skills, and values and promote their cooperation. With the system, job hunters could find a team where members can leverage each other’s strengths and compensate for each other’s weaknesses. Furthermore, companies could effectively evaluate the diversity and cooperativeness of teams for hiring decisions. The evaluation scenario demonstrated that the proposed system could increase both job hunter and recruiter satisfaction levels and social impact.

We propose a deep learning model for a robot to wipe 3D-objects. Wiping of 3D-objects requires recognizing the shapes of objects and planning the motor angle adjustments for tracing the objects. Unlike previous research, our learning model does not require pre-designed computational models of target objects. The robot is able to wipe the objects to be placed by using image, force, and arm joint information. We evaluate the generalization ability of the model by confirming that the robot handles untrained cube and bowl shaped-objects. We also find that it is necessary to use both image and force information to recognize the shape of and wipe 3D objects consistently by comparing changes in the input sensor data to the model. To our knowledge, this is the first work enabling a robot to use learning sensorimotor information alone to trace various unknown 3D-shape.

In this paper, we present a lightweight Deformable Surface Mechanism (DSM) by applying shape-memory alloy (SMA) and sponge for moving objects as a soft actuator. The SMA is driven by heating and cooling processing with the cur-rent flowing. For the SMA, cooling is a process for recovering to original length which consumes time. In order to decrease the recovering time and making the surface deformable, a sponge sheet is applied in the mechanism. We used the cotton thread to sew the SMA into the sponge to manufacture the mechanism. The DSM contains a multi-triangle structure, and each triangle works as an individual actuation unit. By applying this structure and special sewing technique, the sponge sheet can be deformed in a vertical direction when the SMA contracted. While, when the current is turned off, the SMA can be stretched to the original length by the pushing force generated by the sponge. Therefore, a deformable surface mechanism with a rapid response can be achieved. We simulated the changing of uni-Deformable Surface Mechanism (uniDSM), and the experiments were followed to compare with the analyzed results. Additionally, different objects were examined on the DSM to test the conveyance ability.

Although conventional service providers are independent from each other when attending most of the population, demanded services are changing along with the social structure. Especially in the case of Taiwan, the number of co-working families has been increasing, and self-employed households occupy a large proportion of all working forms. Due to their diverse lifestyles and work styles, services that are suitable for personal objectives and that optimize the use of time are required. To meet this demand, it is important to connect people and city facilities to make it easier to provide suitable services. Based on those backgrounds, an innovated personal service platform in Taiwan is proposed, focusing on three factors, including time, place and personal information to connect people and city service facilities. Among various kinds of services, we targets services purchased in cities such as sales, mobility services, health services, government services and so on. It aims to link these services flexibly and dynamically to achieve personal objectives according to each situation. And, it can provide suitable services for a variety of every-day living situations. With this system, people can increase satisfaction and free time, improving life quality while making the economy more dynamic.