46 accepted papers
As the population continues to age, a shortage of caregivers is expected in the future. Dressing assistance, in particular, is crucial for opportunities for social participation. Especially dressing close-fitting garments, such as socks, remains challenging due to the need for fine force adjustments
Humans can achieve diverse in-hand manipulations, such as object pinching and tool use, which often involve simultaneous contact between the object and multiple fingers. This is still an open issue for robotic hands because such dexterous manipulation requires distinguishing between tactile sensatio…
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
To achieve a desired grasping posture (including object position and orientation), multi-finger motions need to be conducted according to the the current touch state. Specifically, when subtle changes happen during correcting the object state, not only proprioception but also tactile information fro
Recently, many attempts have been made to integrate the foundation model with robotics. In most of those attempts, the model recognition results were treated as unique; however, the recognition results required for real robot tasks vary with the task goal. The recognition results of the foundation m
Training robots to operate effectively in environments with uncertain states—such as ambiguous object properties or unpredictable interactions—remains a longstanding challenge in robotics. Imitation learning methods typically rely on successful examples and often neglect failure scenarios where unce
Object grasping in 3D space is crucial for robotic applications. Such tasks are performed by utilizing depth map data acquired from RGB-D images or 3D point cloud data. However, these methods struggle when dealing with transparent objects, as transparency limits sensor performance when predicting de
Safe physical human-robot collaboration is possible with soft robots due to their inherent compliance and low inertia. Soft bodies inherently possess passive compliance, providing adaptability in collaborative tasks because of their deformations; however, the same features add complexity to modeling
Robots that autonomously operate in human living environments require the ability to adapt to unpredictable changes and flexibly handle a variety of tasks. Particularly, coordinated bimanual motions are essential for enabling tasks that are difficult with just one hand, such as grasping bulky object…
In recent years, studies have been actively conducted on combining large language models (LLM) and robotics; however, most have not considered end-to-end feed-back in the robot-motion generation phase. The prediction of deep neural networks must contain errors, it is required to update the trained m…
Performing dexterous tasks with a multi fingered robotic hand remains challenging. Tactile sensors provide touch states and object features for multifingered tasks, yet the variety in shapes, sizes, textures, deformabilities and masses of everyday objects makes the task conditions diverse. Despite t
When humans see a scene, they can roughly imagine the forces applied to objects based on their expe-rience and use them to handle the objects properly. This paper considers transferring this “force-visualization” ability to robots. We hypothesize that a rough force distribution (named “force map”) c…
We developed a modality attention motion generation model on the basis of multi-modality prediction. This model provides interpretability about modality usage and demonstrates robustness against disturbances. We used a hierarchical model consisting of low-level recurrent neural networks (RNNs) for p
We have developed an autonomous robot motion generation model based on distributed and integrated multimodal learning. Since each modality used as a robot's senses, such as image, joint angle, and torque, has a different physical meaning and time characteristic, the generation of autonomous motions…
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 pro…
We propose a haptic shared control system that predicts human manipulation intentions using a neural network and adaptively presents haptic guidance to achieve smooth robot control remotely. Although the haptic shared control has garnered increasing attention as a method to improve operability in re
Anchor-bolt insertion is a peg-in-hole task performed in the construction field for holes in concrete. Efforts have been made to automate this task, but the variable lighting and hole surface conditions, as well as the requirements for short setup and task execution time make the automation challeng
We achieved contact-rich flexible object manipulation, which was difficult to control with vision alone. In the unzipping task we chose as a validation task, the gripper grasps the puller, which hides the bag state such as the direction and amount of deformation behind it, making it difficult to obt…
Sufficiently perceiving the environment is a critical factor in robot motion generation. Although the introduction of deep visual processing models have contributed in extending this ability, existing methods lack in the ability to actively modify what to perceive; humans perform internally during v
We propose a motion generation model that can achieve robust behavior against environmental changes based on language instructions at a low cost. Conventional robots that communicate with humans use a restricted environment and language to build up a mapping between language and motion, and thus nee…
This study achieved bidirectional translation between descriptions and actions using small paired data from different modalities. The ability to mutually generate descriptions and actions is essential for robots to collaborate with humans in their daily lives, which generally requires a large datase
Multi-fingered hands could be used to achieve many dexterous manipulation tasks, similarly to humans, and tactile sensing could enhance the manipulation stability for a variety of objects. However, tactile sensors on multi-fingered hands have a variety of sizes and shapes. Convolutional neural netwo
The construction of 3D point cloud datasets requires a great deal of human effort. Therefore, constructing a largescale 3D point clouds dataset is difficult. In order to remedy this issue, we propose a newly developed point cloud fractal database (PC-FractalDB), which is a novel family of formula-dr…
The paradigm of deep robot learning from demonstrations allows robots to solve complex manipulation tasks by capturing motor skills from given demonstrations; however, collecting demonstrations can be costly. As an alternative, robots can acquire embodiment and motor skills by randomly moving their
Various forms of human knowledge can be explicitly used to enhance deep robot learning from demonstrations. Annotation of subtasks from task segmentation is one type of human symbolism and knowledge. Annotated subtasks can be referred to as action labels, which are more primitive symbols that can be…
There has been an increasing demand for housework robots to handle various objects. It is, however, difficult to achieve object-shape-oriented tasks in conventional research owing to the requirement for dealing with multiple surfaces, invisible area, and occlusion; moreover, robots must perceive sha
A method that enables an industrial robot to accomplish the peg-in-hole task for holes in concrete is proposed. The proposed method involves slightly detaching the peg from the wall, when moving between search positions, to avoid the negative influence of the concrete’s high friction coefficient. It…
A neural network based flexible object manipulation system for a humanoid robot on FPGA is proposed. Although the manipulations of flexible objects using robots attract ever increasing attention since these tasks are the basic and essential activities in our daily life, it has been put into practice…
Robotic applications require both correct task performance and compensation for undefined behaviors. Although deep learning is a promising approach to perform complex tasks, the response to undefined behaviors that are not reflected in the training dataset remains challenging. In a human-robot colla
We propose a promising neural network model with which to acquire a grounded representation of robot actions and the linguistic descriptions thereof. Properly responding to various linguistic expressions, including polysemous words, is an important ability for robots that interact with people via li
Selection of appropriate tools and use of them when performing daily tasks is a critical function for introducing robots for domestic applications. In previous studies, however, adaptability to target objects was limited, making it difficult to accordingly change tools and adjust actions. To manipul
In this study, we report the successful execution of in-air knotting of rope using a dual-arm two-finger robot based on deep learning. Owing to its flexibility, the state of the rope was in constant flux during the operation of the robot. This required the robot control system to dynamically corresp…
The use of tactile information is one of the most important factors for achieving stable in-grasp manipulation. Especially with low-cost robotic hands that provide low-precision control, robust in-grasp manipulation is challenging. Abundant tactile information could provide the required feed-back to…
While robots can learn models to solve many manipulation tasks from raw visual input, they cannot usually use these models to solve new problems. On the other hand, symbolic planning methods such as STRIPS have long been able to solve new problems given only a domain definition and a symbolic goal,…
Performing various in-hand manipulation tasks, without learning each individual task, would enable robots to act more versatile, while reducing the effort for training. However, in general it is difficult to achieve stable in-hand manipulation, because the contact state between the fingertips become…
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…
In this paper we present a neural architecture to learn a bi-directional mapping between actions and language. We implement a Multiple Timescale Long Short-Term Memory (MT-LSTM) network comprised of 7 layers with different timescale factors, to connect actions to language without explicitly learning…
As the complexity of the robot's tasks increases, we can consider many general tasks in a compound form that consists of shorter tasks. Therefore, for robots to generate various tasks, they need to be able to execute shorter tasks in succession, appropriately to the situation. With the design princi…
Distributed tactile sensors on multi-fingered hands can provide high-dimensional information for grasping objects, but it is not clear how to optimally process such abundant tactile information. The current paper explores the possibility of using a morphology-specific convolutional neural network (M…
To ensure that a robot is able to accomplish an extensive range of tasks, it is necessary to achieve a flexible combination of multiple behaviors. This is because the design of task motions suited to each situation would become increasingly difficult as the number of situations and the types of task
We propose a novel deep learning framework for bidirectional translation between robot actions and their linguistic descriptions. Our model consists of two recurrent autoencoders (RAEs). One RAE learns to encode action sequences as fixed-dimensional vectors in a way that allows the sequences to be r
For robots to have a wide range of applications, they must be able to execute numerous tasks. However, recent studies into robot manipulation using deep neural networks (DNN) have primarily focused on single tasks. Therefore, we investigate a robot manipulation model that uses DNNs and can execute l…
We propose a practical state-of-the-art method to develop a machine-learning-based humanoid robot that can work as a production line worker. The proposed approach provides an intuitive way to collect data and exhibits the following characteristics: task performing capability, task reiteration abilit
In recent years there has been increased interest in studies that explore integrative learning of language and other modalities by using neural network models. However, for practical application to human-robot interaction, the acquired semantic structure between language and meaning has to be availa…
We propose a method for realizing effective dynamic motion learning in a flexible-joint robot using motor babbling. Flexible-joint robots have recently attracted attention because of their adaptiveness, safety, and, in particular, dynamic motions. It is difficult to control robots that require dynam…
In this study, we propose a neural network based model for learning a robot's drawing sequences in an unsupervised manner. We focus on the ability to learn visual-motor relationships, which can work as a reusable memory in association of drawing motion from a picture image. Assuming that a humanoid…