110 citations · 261 across the 7 of their papers we have counts for
7 papers
Surface-Aligned Neural Radiance Fields for Controllable 3D Human Synthesis
Tianhan Xu, Yasuhiro Fujita, Eiichi Matsumoto
We propose a new method for reconstructing controllable implicit 3D human models from sparse multi-view RGB videos. Our method defines the neural scene representation on the mesh s…
Distributed Reinforcement Learning of Targeted Grasping with Active Vision for Mobile Manipulators
Yasuhiro Fujita, Kota Uenishi, Avinash Ummadisingu +3
Developing personal robots that can perform a diverse range of manipulation tasks in unstructured environments necessitates solving several challenges for robotic grasping systems.…
Learning Latent State Spaces for Planning through Reward Prediction
Aaron Havens, Yi Ouyang, Prabhat Nagarajan +1
Model-based reinforcement learning methods typically learn models for high-dimensional state spaces by aiming to reconstruct and predict the original observations. However, drawing…
ChainerRL: A Deep Reinforcement Learning Library
Yasuhiro Fujita, Prabhat Nagarajan, Toshiki Kataoka +1
In this paper, we introduce ChainerRL, an open-source deep reinforcement learning (DRL) library built using Python and the Chainer deep learning framework. ChainerRL implements a c…
A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning
Yoshihiro Nagano, Shoichiro Yamaguchi, Yasuhiro Fujita +1
Hyperbolic space is a geometry that is known to be well-suited for representation learning of data with an underlying hierarchical structure. In this paper, we present a novel hype…
Model-Based Reinforcement Learning via Meta-Policy Optimization
Ignasi Clavera, Jonas Rothfuss, John Schulman +3
Model-based reinforcement learning approaches carry the promise of being data efficient. However, due to challenges in learning dynamics models that sufficiently match the real-wor…
Clipped Action Policy Gradient
Yasuhiro Fujita, Shin-ichi Maeda
Many continuous control tasks have bounded action spaces. When policy gradient methods are applied to such tasks, out-of-bound actions need to be clipped before execution, while po…