156 citations · 246 across the 43 of their papers we have counts for
10 papers · 1 filter
Binarized P-Network: Deep Reinforcement Learning of Robot Control from Raw Images on FPGA
Yuki Kadokawa, Yoshihisa Tsurumine, Takamitsu Matsubara
This paper explores a Deep Reinforcement Learning (DRL) approach for designing image-based control for edge robots to be implemented on Field Programmable Gate Arrays (FPGAs). Alth…
Geometric Value Iteration: Dynamic Error-Aware KL Regularization for Reinforcement Learning
Toshinori Kitamura, Lingwei Zhu, Takamitsu Matsubara
The recent boom in the literature on entropy-regularized reinforcement learning (RL) approaches reveals that Kullback-Leibler (KL) regularization brings advantages to RL algorithms…
Cautious Policy Programming: Exploiting KL Regularization in Monotonic Policy Improvement for Reinforcement Learning
Lingwei Zhu, Toshinori Kitamura, Takamitsu Matsubara
In this paper, we propose cautious policy programming (CPP), a novel value-based reinforcement learning (RL) algorithm that can ensure monotonic policy improvement during learning.…
Cautious Actor-Critic
Lingwei Zhu, Toshinori Kitamura, Takamitsu Matsubara
The oscillating performance of off-policy learning and persisting errors in the actor-critic (AC) setting call for algorithms that can conservatively learn to suit the stability-cr…
Variational Policy Search using Sparse Gaussian Process Priors for Learning Multimodal Optimal Actions
Hikaru Sasaki, Takamitsu Matsubara
Policy search reinforcement learning has been drawing much attention as a method of learning a robot control policy. In particular, policy search using such non-parametric policies…
Robust shape estimation with false-positive contact detection
Kazuki Shibata, Tatsuya Miyano, Tomohiko Jimbo +1
We propose a means of omni-directional contact detection using accelerometers instead of tactile sensors for object shape estimation using touch. Unlike tactile sensors, our contac…
Tactile Perception based on Injected Vibration in Soft Sensor
Naoto Komeno, Takamitsu Matsubara
Tactile perception using vibration sensation helps robots recognize their environment's physical properties and perform complex tasks. A sliding motion is applied to target objects…
Sample-efficient Gear-ratio Optimization for Biomechanical Energy Harvester
Taisuke Kobayashi, Yutaro Ikawa, Takamitsu Matsubara
The biomechanical energy harvester is expected to harvest the electric energies from human motions. A tradeoff between harvesting energy and keeping the user's natural movements sh…
Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport
Kazuki Shibata, Tomohiko Jimbo, Takamitsu Matsubara
In this paper, we explore a multi-agent reinforcement learning approach to address the design problem of communication and control strategies for multi-agent cooperative transport.…
Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies
Hanbit Oh, Hikaru Sasaki, Brendan Michael +1
Scenarios requiring humans to choose from multiple seemingly optimal actions are commonplace, however standard imitation learning often fails to capture this behavior. Instead, an…