9 accepted papers
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
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…
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…
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…