50 citations · 127 across the 16 of their papers we have counts for
16 papers
A Peg-in-hole Task Strategy for Holes in Concrete
André Yuji Yasutomi, Hiroki Mori, Tetsuya Ogata
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 wal…
Visual Spatial Attention and Proprioceptive Data-Driven Reinforcement Learning for Robust Peg-in-Hole Task Under Variable Conditions
André Yuji Yasutomi, Hideyuki Ichiwara, Hiroshi Ito +2
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 h…
Real-time Motion Generation and Data Augmentation for Grasping Moving Objects with Dynamic Speed and Position Changes
Kenjiro Yamamoto, Hiroshi Ito, Hideyuki Ichiwara +2
While deep learning enables real robots to perform complex tasks had been difficult to implement in the past, the challenge is the enormous amount of trial-and-error and motion tea…
Learning Multimodal Attention for Manipulating Deformable Objects with Changing States
Namiko Saito, Mayu Tatsumi, Ayuna Kubo +4
To support humans in their daily lives, robots are required to autonomously learn, adapt to objects and environments, and perform the appropriate actions. We tackled on the task of…
Interactively Robot Action Planning with Uncertainty Analysis and Active Questioning by Large Language Model
Kazuki Hori, Kanata Suzuki, Tetsuya Ogata
The application of the Large Language Model (LLM) to robot action planning has been actively studied. The instructions given to the LLM by natural language may include ambiguity an…
Deep Predictive Learning: Motion Learning Concept inspired by Cognitive Robotics
Kanata Suzuki, Hiroshi Ito, Tatsuro Yamada +2
Bridging the gap between motion models and reality is crucial by using limited data to deploy robots in the real world. Deep learning is expected to be generalized to diverse situa…
Force Map: Learning to Predict Contact Force Distribution from Vision
Ryo Hanai, Yukiyasu Domae, Ixchel G. Ramirez-Alpizar +2
When humans see a scene, they can roughly imagine the forces applied to objects based on their experience and use them to handle the objects properly. This paper considers transfer…
Deep Active Visual Attention for Real-time Robot Motion Generation: Emergence of Tool-body Assimilation and Adaptive Tool-use
Hyogo Hiruma, Hiroshi Ito, Hiroki Mori +1
Sufficiently perceiving the environment is a critical factor in robot motion generation. Although the introduction of deep visual processing models have contributed in extending th…
Multi-Fingered In-Hand Manipulation with Various Object Properties Using Graph Convolutional Networks and Distributed Tactile Sensors
Satoshi Funabashi, Tomoki Isobe, Fei Hongyi +4
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…
Learning Bidirectional Translation between Descriptions and Actions with Small Paired Data
Minori Toyoda, Kanata Suzuki, Yoshihiko Hayashi +1
This study achieved bidirectional translation between descriptions and actions using small paired data from different modalities. The ability to mutually generate descriptions and…
Collision-free Path Planning in the Latent Space through cGANs
Tomoki Ando, Hiroki Mori, Ryota Torishima +4
We show a new method for collision-free path planning by cGANs by mapping its latent space to only the collision-free areas of the robot joint space. Our method simply provides thi…
Learning-based Collision-free Planning on Arbitrary Optimization Criteria in the Latent Space through cGANs
Tomoki Ando, Hiroto Iino, Hiroki Mori +5
We propose a new method for collision-free planning using Conditional Generative Adversarial Networks (cGANs) to transform between the robot's joint space and a latent space that c…
Guided Visual Attention Model Based on Interactions Between Top-down and Bottom-up Information for Robot Pose Prediction
Hyogo Hiruma, Hiroki Mori, Hiroshi Ito +1
Deep robot vision models are widely used for recognizing objects from camera images, but shows poor performance when detecting objects at untrained positions. Although such problem…
Contact-Rich Manipulation of a Flexible Object based on Deep Predictive Learning using Vision and Tactility
Hideyuki Ichiwara, Hiroshi Ito, Kenjiro Yamamoto +2
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…
Emergence of sensory attenuation based upon the free-energy principle
Hayato Idei, Wataru Ohata, Yuichi Yamashita +2
The brain attenuates its responses to self-produced exteroceptions (e.g., we cannot tickle ourselves). Is this phenomenon, known as sensory attenuation, enabled innately, or acquir…
Multisensory Learning Framework for Robot Drumming
A. Barsky, C. Zito, H. Mori +2
The hype about sensorimotor learning is currently reaching high fever, thanks to the latest advancement in deep learning. In this paper, we present an open-source framework for col…