3 citations · 6 across the 5 of their papers we have counts for
6 papers
World-Model-Based Control for Industrial box-packing of Multiple Objects using NewtonianVAE
Yusuke Kato, Ryo Okumura, Tadahiro Taniguchi
The process of industrial box-packing, which involves the accurate placement of multiple objects, requires high-accuracy positioning and sequential actions. When a robot is tasked…
Learning Compliant Stiffness by Impedance Control-Aware Task Segmentation and Multi-objective Bayesian Optimization with Priors
Masashi Okada, Mayumi Komatsu, Ryo Okumura +1
Rather than traditional position control, impedance control is preferred to ensure the safe operation of industrial robots programmed from demonstrations. However, variable stiffne…
Patch-based Object-centric Transformers for Efficient Video Generation
Wilson Yan, Ryo Okumura, Stephen James +1
In this work, we present Patch-based Object-centric Video Transformer (POVT), a novel region-based video generation architecture that leverages object-centric information to effici…
Multi-View Dreaming: Multi-View World Model with Contrastive Learning
Akira Kinose, Masashi Okada, Ryo Okumura +1
In this paper, we propose Multi-View Dreaming, a novel reinforcement learning agent for integrated recognition and control from multi-view observations by extending Dreaming. Most…
Tactile-Sensitive NewtonianVAE for High-Accuracy Industrial Connector Insertion
Ryo Okumura, Nobuki Nishio, Tadahiro Taniguchi
An industrial connector insertion task requires submillimeter positioning and grasp pose compensation for a plug. Thus, highly accurate estimation of the relative pose between a pl…
Domain-Adversarial and Conditional State Space Model for Imitation Learning
Ryo Okumura, Masashi Okada, Tadahiro Taniguchi
State representation learning (SRL) in partially observable Markov decision processes has been studied to learn abstract features of data useful for robot control tasks. For SRL, a…