156 citations · 246 across the 43 of their papers we have counts for
13 papers · 1 filter
Learning Locally, Communicating Globally: Reinforcement Learning of Multi-robot Task Allocation for Cooperative Transport
Kazuki Shibata, Tomohiko Jimbo, Tadashi Odashima +2
We consider task allocation for multi-object transport using a multi-robot system, in which each robot selects one object among multiple objects with different and unknown weights.…
Deep reinforcement learning of event-triggered communication and consensus-based control for distributed cooperative transport
Kazuki Shibata, Tomohiko Jimbo, Takamitsu Matsubara
In this paper, we present a solution to a design problem of control strategies for multi-agent cooperative transport. Although existing learning-based methods assume that the numbe…
Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies for Robot Manipulation
Hanbit Oh, Hikaru Sasaki, Brendan Michael +1
Humans demonstrate a variety of interesting behavioral characteristics when performing tasks, such as selecting between seemingly equivalent optimal actions, performing recovery ac…
Deep Koopman with Control: Spectral Analysis of Soft Robot Dynamics
Naoto Komeno, Brendan Michael, Katharina Küchler +2
Soft robots are challenging to model and control as inherent non-linearities (e.g., elasticity and deformation), often requires complex explicit physics-based analytical modeling (…
Physically Consistent Preferential Bayesian Optimization for Food Arrangement
Yuhwan Kwon, Yoshihisa Tsurumine, Takeshi Shimmura +2
This paper considers the problem of estimating a preferred food arrangement for users from interactive pairwise comparisons using Computer Graphics (CG)-based dish images. As a foo…
Goal-Aware Generative Adversarial Imitation Learning from Imperfect Demonstration for Robotic Cloth Manipulation
Yoshihisa Tsurumine, Takamitsu Matsubara
Generative Adversarial Imitation Learning (GAIL) can learn policies without explicitly defining the reward function from demonstrations. GAIL has the potential to learn policies wi…
Randomized-to-Canonical Model Predictive Control for Real-world Visual Robotic Manipulation
Tomoya Yamanokuchi, Yuhwan Kwon, Yoshihisa Tsurumine +3
Many works have recently explored Sim-to-real transferable visual model predictive control (MPC). However, such works are limited to one-shot transfer, where real-world data must b…
Cyclic Policy Distillation: Sample-Efficient Sim-to-Real Reinforcement Learning with Domain Randomization
Yuki Kadokawa, Lingwei Zhu, Yoshihisa Tsurumine +1
Deep reinforcement learning with domain randomization learns a control policy in various simulations with randomized physical and sensor model parameters to become transferable to…
Enforcing KL Regularization in General Tsallis Entropy Reinforcement Learning via Advantage Learning
Lingwei Zhu, Zheng Chen, Eiji Uchibe +1
Maximum Tsallis entropy (MTE) framework in reinforcement learning has gained popularity recently by virtue of its flexible modeling choices including the widely used Shannon entrop…
-Munchausen Reinforcement Learning
Lingwei Zhu, Zheng Chen, Eiji Uchibe +1
The recently successful Munchausen Reinforcement Learning (M-RL) features implicit Kullback-Leibler (KL) regularization by augmenting the reward function with logarithm of the curr…
Disturbance-Injected Robust Imitation Learning with Task Achievement
Hirotaka Tahara, Hikaru Sasaki, Hanbit Oh +2
Robust imitation learning using disturbance injections overcomes issues of limited variation in demonstrations. However, these methods assume demonstrations are optimal, and that p…
Gaussian Process Self-triggered Policy Search in Weakly Observable Environments
Hikaru Sasaki, Terushi Hirabayashi, Kaoru Kawabata +1
The environments of such large industrial machines as waste cranes in waste incineration plants are often weakly observable, where little information about the environmental state…
AdaTerm: Adaptive T-Distribution Estimated Robust Moments for Noise-Robust Stochastic Gradient Optimization
Wendyam Eric Lionel Ilboudo, Taisuke Kobayashi, Takamitsu Matsubara
With the increasing practicality of deep learning applications, practitioners are inevitably faced with datasets corrupted by noise from various sources such as measurement errors,…