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20192024
most citedMulti-Fingered In-Hand Manipulation with Various Object Properties Using Graph Convolutional Networks and Distributed Tactile Sensors

50 citations · 127 across the 16 of their papers we have counts for

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Showing 2022Show all

6 papers · 1 filter

cs.RO2022

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…

cs.RO2022★ 50 cited

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…

cs.RO2022★ 8 cited

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…

cs.RO2022★ 1 cited

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…

cs.RO2022

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…

cs.RO2022★ 1 cited

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…