activity
20182022
most citedPeriodic Intra-Ensemble Knowledge Distillation for Reinforcement Learning

1 citations · 1 across the 3 of their papers we have counts for

collaborators

6 papers

cs.RO2022

F1 Hand: A Versatile Fixed-Finger Gripper for Delicate Teleoperation and Autonomous Grasping

Guilherme Maeda, Naoki Fukaya, Shin-ichi Maeda

Teleoperation is often limited by the ability of an operator to react and predict the behavior of the robot as it interacts with the environment. For example, to grasp small object…

cs.RO2022

Blending Primitive Policies in Shared Control for Assisted Teleoperation

Guilherme Maeda

Movement primitives have the property to accommodate changes in the robot state while maintaining attraction to the original policy. As such, we investigate the use of primitives a…

cs.RO2020

Visual Task Progress Estimation with Appearance Invariant Embeddings for Robot Control and Planning

Guilherme Maeda, Joni Väätäinen, Hironori Yoshida

One of the challenges of full autonomy is to have a robot capable of manipulating its current environment to achieve another environment configuration. This paper is a step towards…

cs.LG20201 cited

Periodic Intra-Ensemble Knowledge Distillation for Reinforcement Learning

Zhang-Wei Hong, Prabhat Nagarajan, Guilherme Maeda

Off-policy ensemble reinforcement learning (RL) methods have demonstrated impressive results across a range of RL benchmark tasks. Recent works suggest that directly imitating expe…

cs.RO2019

Phase Portraits as Movement Primitives for Fast Humanoid Robot Control

Guilherme Maeda, Okan Koc, Jun Morimoto

Currently, usual approaches for fast robot control are largely reliant on solving online optimal control problems. Such methods are known to be computationally intensive and sensit…

cs.RO2018

Optimizing Execution of Dynamic Goal-Directed Robot Movements with Learning Control

Okan Koc, Guilherme Maeda, Jan Peters

Highly dynamic tasks that require large accelerations and precise tracking usually rely on accurate models and/or high gain feedback. While kinematic optimization allows for effici…