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Kenzo Lobos-Tsunekawa

2 accepted papers

2020

Point Cloud Based Reinforcement Learning for Sim-to-Real and Partial Observability in Visual Navigation

IROS 2020poster

Reinforcement Learning (RL), among other learning-based methods, represents powerful tools to solve complex robotic tasks (e.g., actuation, manipulation, navigation, etc.), with the need for real-world data to train these systems as one of its most important limitations. The use of simulators is one…

Cited by 13Source ↗Scholar ↗
2018

Visual Navigation for Biped Humanoid Robots Using Deep Reinforcement Learning

RA-L 2018

In this letter, we propose a map-less visual navigation system for biped humanoid robots, which extracts information from color images to derive motion commands using deep reinforcement learning (DRL). The map-less visual navigation policy is trained using the Deep Deterministic Policy Gradients (DD

Cited by 94Source ↗Scholar ↗