39 citations · 133 across the 15 of their papers we have counts for
16 papers
EasyControlEdge: A Foundation-Model Fine-Tuning for Edge Detection
Hiroki Nakamura, Hiroto Iino, Masashi Okada +1
We propose EasyControlEdge, adapting an image-generation foundation model to edge detection. In real-world edge detection (e.g., floor-plan walls, satellite roads/buildings, and me…
A Contact Model based on Denoising Diffusion to Learn Variable Impedance Control for Contact-rich Manipulation
Masashi Okada, Mayumi Komatsu, Tadahiro Taniguchi
In this paper, a novel approach is proposed for learning robot control in contact-rich tasks such as wiping, by developing Diffusion Contact Model (DCM). Previous methods of learni…
Representation Synthesis by Probabilistic Many-Valued Logic Operation in Self-Supervised Learning
Hiroki Nakamura, Masashi Okada, Tadahiro Taniguchi
In this paper, we propose a new self-supervised learning (SSL) method for representations that enable logic operations. Representation learning has been applied to various tasks, s…
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…
Online Re-Planning and Adaptive Parameter Update for Multi-Agent Path Finding with Stochastic Travel Times
Atsuyoshi Kita, Nobuhiro Suenari, Masashi Okada +1
This study explores the problem of Multi-Agent Path Finding with continuous and stochastic travel times whose probability distribution is unknown. Our purpose is to manage a group…
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…
Representation Uncertainty in Self-Supervised Learning as Variational Inference
Hiroki Nakamura, Masashi Okada, Tadahiro Taniguchi
In this study, a novel self-supervised learning (SSL) method is proposed, which considers SSL in terms of variational inference to learn not only representation but also representa…
DreamingV2: Reinforcement Learning with Discrete World Models without Reconstruction
Masashi Okada, Tadahiro Taniguchi
The present paper proposes a novel reinforcement learning method with world models, DreamingV2, a collaborative extension of DreamerV2 and Dreaming. DreamerV2 is a cutting-edge mod…
Visual Exploration System for Analyzing Trends in Annual Recruitment Using Time-varying Graphs
Toshiyuki T. Yokoyama, Masashi Okada, Tadahiro Taniguchi
Annual recruitment data of new graduates are manually analyzed by human resources specialists (HR) in industries, which signifies the need to evaluate the recruitment strategy of H…
Dreaming: Model-based Reinforcement Learning by Latent Imagination without Reconstruction
Masashi Okada, Tadahiro Taniguchi
In the present paper, we propose a decoder-free extension of Dreamer, a leading model-based reinforcement learning (MBRL) method from pixels. Dreamer is a sample- and cost-efficien…
PlaNet of the Bayesians: Reconsidering and Improving Deep Planning Network by Incorporating Bayesian Inference
Masashi Okada, Norio Kosaka, Tadahiro Taniguchi
In the present paper, we propose an extension of the Deep Planning Network (PlaNet), also referred to as PlaNet of the Bayesians (PlaNet-Bayes). There has been a growing demand in…
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…
Multi-person Pose Tracking using Sequential Monte Carlo with Probabilistic Neural Pose Predictor
Masashi Okada, Shinji Takenaka, Tadahiro Taniguchi
It is an effective strategy for the multi-person pose tracking task in videos to employ prediction and pose matching in a frame-by-frame manner. For this type of approach, uncertai…
Variational Inference MPC for Bayesian Model-based Reinforcement Learning
Masashi Okada, Tadahiro Taniguchi
In recent studies on model-based reinforcement learning (MBRL), incorporating uncertainty in forward dynamics is a state-of-the-art strategy to enhance learning performance, making…
Acceleration of Gradient-based Path Integral Method for Efficient Optimal and Inverse Optimal Control
Masashi Okada, Tadahiro Taniguchi
This paper deals with a new accelerated path integral method, which iteratively searches optimal controls with a small number of iterations. This study is based on the recent obser…
Path Integral Networks: End-to-End Differentiable Optimal Control
Masashi Okada, Luca Rigazio, Takenobu Aoshima
In this paper, we introduce Path Integral Networks (PI-Net), a recurrent network representation of the Path Integral optimal control algorithm. The network includes both system dyn…