activity
20192022
most citedHow to select and use tools? : Active Perception of Target Objects Using Multimodal Deep Learning

50 citations · 85 across the 5 of their papers we have counts for

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5 papers · 1 filter

cs.RO20221 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.RO202150 cited

How to select and use tools? : Active Perception of Target Objects Using Multimodal Deep Learning

Namiko Saito, Tetsuya Ogata, Satoshi Funabashi +2

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, ad…

cs.RO202119 cited

Embodying Pre-Trained Word Embeddings Through Robot Actions

Minori Toyoda, Kanata Suzuki, Hiroki Mori +2

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…

cs.RO202111 cited

Spatial Attention Point Network for Deep-learning-based Robust Autonomous Robot Motion Generation

Hideyuki Ichiwara, Hiroshi Ito, Kenjiro Yamamoto +2

Deep learning provides a powerful framework for automated acquisition of complex robotic motions. However, despite a certain degree of generalization, the need for vast amounts of…

cs.RO20194 cited

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…