77 citations · 109 across the 11 of their papers we have counts for
11 papers
Unsupervised Neural Motion Retargeting for Humanoid Teleoperation
Satoshi Yagi, Mitsunori Tada, Eiji Uchibe +3
This study proposes an approach to human-to-humanoid teleoperation using GAN-based online motion retargeting, which obviates the need for the construction of pairwise datasets to i…
Reward-Punishment Reinforcement Learning with Maximum Entropy
Jiexin Wang, Eiji Uchibe
We introduce the ``soft Deep MaxPain'' (softDMP) algorithm, which integrates the optimization of long-term policy entropy into reward-punishment reinforcement learning objectives.…
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…
Model-Based Imitation Learning Using Entropy Regularization of Model and Policy
Eiji Uchibe
Approaches based on generative adversarial networks for imitation learning are promising because they are sample efficient in terms of expert demonstrations. However, training a ge…
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…