6 accepted papers
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…
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…
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…
Depth estimation with monocular cameras is a cheap and promising solution for autonomous vehicles and robots. Even though there are many approaches in the literature, the issue of estimating depth of objects with low optic flow (low parallax) still remains. This work proposes a new two-view monocula
Fast and accurate depth estimation is essential for autonomous driving vehicles and autonomous robots. Monocular cameras have been expected to be widely applied due to their lower cost, but since they can't provide depth directly, fast and accurate monocular depth estimation methods are required. Th…